119413 - FUNDAMENTALS OF DIGITAL ENGINEERING APPLIED TO AGRICULTURE
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12
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Learning objectives
The objective of the "SENSOR" module of the Fundamentals of digital engineering applied to agriculture course is to provide the student with full knowledge of both the correct metrological language and the functioning of the main measuring instruments for digital agriculture applications. The sensors will be analyzed both considering the design process and the operating principle.
The expected results according to the Dublin descriptors are the following:
Knowledge and understanding
Know the definitions of the static and dynamic meter characteristics, know the definitions of the units of measure, understand the meaning of probability distribution linked to the measure in order to be able to define the extended uncertainty, understand the concept of sampling and analog-digital conversion, includes the operation of a measuring instrument for the electrical evaluation of mechanical and thermal quantities and in digital agriculture applications.
Ability to apply correct knowledge and understanding
Having an understanding of the scientific approach in the field of measurements. Have the ability to independently carry out a calibration and associate the correct uncertainty in the function of the instruments used. Understanding the significance of the results through applied statistics. Have the ability to carry out a dynamic study of first and second order measuring instruments.
Judgment skills
The student will be able to evaluate the sensors most suitable for a given use and will be able to select the correct application in the world of agriculture.
Communication skills
The student will acquire the skills to be able to argue the metrological concepts and uncertainty in the exam, as well as the operating principle of sensors and the importance of the world of measurements in the agricultural field.
Learning skills
The student will acquire the skills to be able to independently deepen the study of advanced sensors or the use of such as artificial intelligence, in addition to the basic ones seen above.
MODULE II
GIANLUCA COLTRINARI
First Semester
6
ING-IND/12
Learning objectives
The objective of the "SENSOR" module of the Fundamentals of digital engineering applied to agriculture course is to provide the student with full knowledge of both the correct metrological language and the functioning of the main measuring instruments for digital agriculture applications. The sensors will be analyzed both considering the design process and the operating principle.
The expected results according to the Dublin descriptors are the following:
Knowledge and understanding
Know the definitions of the static and dynamic meter characteristics, know the definitions of the units of measure, understand the meaning of probability distribution linked to the measure in order to be able to define the extended uncertainty, understand the concept of sampling and analog-digital conversion, includes the operation of a measuring instrument for the electrical evaluation of mechanical and thermal quantities and in digital agriculture applications.
Ability to apply correct knowledge and understanding
Having an understanding of the scientific approach in the field of measurements. Have the ability to independently carry out a calibration and associate the correct uncertainty in the function of the instruments used. Understanding the significance of the results through applied statistics. Have the ability to carry out a dynamic study of first and second order measuring instruments.
Judgment skills
The student will be able to evaluate the sensors most suitable for a given use and will be able to select the correct application in the world of agriculture.
Communication skills
The student will acquire the skills to be able to argue the metrological concepts and uncertainty in the exam, as well as the operating principle of sensors and the importance of the world of measurements in the agricultural field.
Learning skills
The student will acquire the skills to be able to independently deepen the study of advanced sensors or the use of such as artificial intelligence, in addition to the basic ones seen above.
Knowledge and understanding
Students will gain a solid understanding of the fundamentals of programming in Python and the basics of mechatronics and the Internet of Things (IoT). They will be able to understand and explain the theoretical principles governing the integration of mechanical, electronic and software components for applications in agriculture and beyond.
Applying knowledge and understanding
Students will be able to apply their acquired skills in Python programming to develop practical mechatronics projects using Raspberry Pi. They will be able to design, implement and test digital solutions that combine sensors, actuators and communication modules, with a focus on agricultural applications.
Making judgements
Students will develop the ability to critically analyze proposed solutions to specific digital engineering problems applied to agriculture. They will be able to evaluate the effectiveness of their mechatronic and IoT solutions by considering various technical factors and make autonomous decisions regarding the most appropriate implementations.
Communication skills
Students will be able to effectively communicate the results of their projects, both orally and in writing, using appropriate technical language. They will be able to document and present their work clearly and coherently, making the technological solutions adopted and the results obtained understandable even to non-specialists.
Learning skills
Students will develop the ability to independently learn new techniques and tools in programming, mechatronics and IoT. They will be able to continuously update themselves, successfully tackling new technological and application challenges, thanks to a solid methodological and practical foundation.
The course will be divided mainly into 2 parts, a first part where students will be taught Python programming, and a second part, where students will use the knowledge gained to tackle mechatronics and IoT projects.
examMode
the objective of the exam is to verify that the student is able to deal with digital agriculture projects, related to mechatronics and IoT.
books
No text adopted
mode
Frontal lecture supported by slides and classroom exercises
classRoomMode
Classroom attendance recommended, and almost essential in the last half of the course for conducting exercises with instrumentation
bibliography
No reference bibliography to report
119466 - INNOVATION IN THE MANAGEMENT OF PHYTOSANITARY ISSUES
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6
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Learning objectives
The aim of the course is to provide the basis for learning how to assess and monitor pest (entomology module) and pathogen (pathology module) risks using advanced techniques, including monitoring and forecasting systems, and innovative diagnostic tools. At the end of the course, students will be able to develop innovative and sustainable pest management strategies, integrating biological, chemical and cultural techniques. They will acquire skills in the use of advanced technologies to improve the effectiveness and efficiency of plant health practices and develop communication skills to effectively transfer knowledge and innovations in plant health to different stakeholders, including farmers, technicians and land managers.
Knowledge and understanding
Demonstrate a thorough knowledge of the theories and principles governing plant health issues and the innovative solutions available to manage them.
Applying knowledge and understanding
Apply theoretical and methodological knowledge to the diagnosis and management of concrete phytosanitary problems, using advanced technological tools.
Making judgements
Make autonomous and critical judgements regarding different options for the management of plant health problems, taking into account practical, economic and environmental implications.
Communication skills
Use the correct technical-scientific terminology when describing course topics. Ability to synthesize and communicate effectively to specialists and non-specialists.
Learning skills
Demonstrate the ability to learn independently and continuously, keeping abreast of the latest innovations and developments in the field of pest management.
MODULE II
MARIO CONTARINI
First Semester
3
AGR/11
Learning objectives
The aim of the course is to provide the basis for learning how to assess and monitor pest (entomology module) and pathogen (pathology module) risks using advanced techniques, including monitoring and forecasting systems, and innovative diagnostic tools. At the end of the course, students will be able to develop innovative and sustainable pest management strategies, integrating biological, chemical and cultural techniques. They will acquire skills in the use of advanced technologies to improve the effectiveness and efficiency of plant health practices and develop communication skills to effectively transfer knowledge and innovations in plant health to different stakeholders, including farmers, technicians and land managers.
Knowledge and understanding
Demonstrate a thorough knowledge of the theories and principles governing plant health issues and the innovative solutions available to manage them.
Applying knowledge and understanding
Apply theoretical and methodological knowledge to the diagnosis and management of concrete phytosanitary problems, using advanced technological tools.
Making judgements
Make autonomous and critical judgements regarding different options for the management of plant health problems, taking into account practical, economic and environmental implications.
Communication skills
Use the correct technical-scientific terminology when describing course topics. Ability to synthesize and communicate effectively to specialists and non-specialists.
Learning skills
Demonstrate the ability to learn independently and continuously, keeping abreast of the latest innovations and developments in the field of pest management.
1. Importance of integrated pest management
2. Traditional monitoring strategies and potential innovations
3. Decision support systems (DSS)
Mathematical models for the description and prediction of insect populations
Statistical and mathematical models for the study of species distribution (MAXENT, Random Forest etc)
Measurement and estimation of insect populations
Monitoring strategies with innovative traps
Case studies
4. Proximal sensing in monitoring of main insects in agriculture and forestry
Monitoring with automated traps
YOLO technology and machine learning for pests detection and recognition
Case studies
5. Remote sensing in monitoring of main insects in agriculture and forestry
UAVs and sensors, data collection and processing
The use of satellite-collected data for assessing the activity of phytophagous insects
Case studies
6. Apps for insect species recognition (citizen science)
examMode
The evaluation of knowledge will take place through a final oral exam related to the course programme and the seminars held.
books
Students will be provided with ppt slides. The study will be integrated with scientific papers provided by the teacher
mode
Classes will take place in presence. However streaming will allow students to take the class
classRoomMode
Attendence is not required but strongly recommended
bibliography
Below are some of the scientific publications suggested to students:
- Review of CLIMEX and MaxEnt for studying species distribution in South Korea - Journal of Asia-Pacific Biodiversity (2018) - Dae-hyeon Byeon, Sunghoon Jung, Wang-Hee Lee
- A review: application of remote sensing as a promising strategy for insect pests and diseases management - Environmental Science and Pollution Research (2020) - Nesreen M. Abd El-Ghany, Shadia E. Abd El-Aziz, Shahira S. Marei
- Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review - Forests (2022) - André Duarte, Nuno Borralho, Pedro Cabral, Mário Caetano
- Automatic Detection and Monitoring of Insect Pests—A Review - Agriculture (2020) - Matheus Cardim Ferreira Lima, Maria Elisa Damascena de Almeida Leandro, Constantino Valero, Luis Carlos Pereira Coronel, Clara Oliva Gonçalves Bazzo
MODULE II
ANGELO MAZZAGLIA
First Semester
3
AGR/12
Learning objectives
The aim of the course is to provide the basis for learning how to assess and monitor pest (entomology module) and pathogen (pathology module) risks using advanced techniques, including monitoring and forecasting systems, and innovative diagnostic tools. At the end of the course, students will be able to develop innovative and sustainable pest management strategies, integrating biological, chemical and cultural techniques. They will acquire skills in the use of advanced technologies to improve the effectiveness and efficiency of plant health practices and develop communication skills to effectively transfer knowledge and innovations in plant health to different stakeholders, including farmers, technicians and land managers.
Knowledge and understanding
Demonstrate a thorough knowledge of the theories and principles governing plant health issues and the innovative solutions available to manage them.
Applying knowledge and understanding
Apply theoretical and methodological knowledge to the diagnosis and management of concrete phytosanitary problems, using advanced technological tools.
Making judgements
Make autonomous and critical judgements regarding different options for the management of plant health problems, taking into account practical, economic and environmental implications.
Communication skills
Use the correct technical-scientific terminology when describing course topics. Ability to synthesize and communicate effectively to specialists and non-specialists.
Learning skills
Demonstrate the ability to learn independently and continuously, keeping abreast of the latest innovations and developments in the field of pest management.
Importance of digital approach and technological innovations in plant disease management.
Detection and monitoring of diseases and pathogens:
• Critical approach to diagnosis: when traditional techniques are enough and when not
• Advanced diagnostic methods:
o immunological techniques (ELISA, DBTIA, Lateral flow, etc.)
o molecular (standard PCR, Real-Time PCR (qPCR), loop-mediated isothermal amplification (LAMP), digital droplet PCR (ddPCR).
o biosensors
Assessment of the incidence of the disease and the damage caused by remote sensing:
satellite images, ultralight aircrafts and drones.
Assessment of structural damages to trees and risk related to plant stability in urban environments and control:
VTA, instrumental diagnosis (resistograph, tomograph, pulse hammer, Pressler’s pacifier, fracking meter, use of infrasound, Ground Probing Radar (GPR), Compressed Air Digging Systems (Air-Spade®, Dendrotherapy).
Bioinformatics approach to understanding pathogen biology through omics sciences (genomics, transcriptomics, proteomics, etc.);
Strategies for disease prevention and management in precision agriculture:
• forecast models
• monitoring networks
• decision support systems (DSS) for plant protection.
Optimization of the distribution of active ingredients: advantages and problems
Latest tools in plant protection:
• the genome editing
• nanotechnologies in plant protection
Disease control and improvement of their resilience to stress through biological agents:
• antagonistic micro-organisms,
• natural microbial communities (endophytes and epiphytes),
• supporting micro-organisms: PGPR and mycorrhizae
examMode
The exam, as a whole, will aim to verify the following educational objectives:
KNOWLEDGE AND ABILITY TO UNDERSTAND
The student must demonstrate to have acquired a comprehensive knowledge of the basics of plant protection in the context of digital agriculture; have clearly understood the basics of vegetal pathology. The student must demonstrate that he understood the ways of occurrence and spread of plant diseases and how to evaluate them with innovative tools; to have understood the main innovative diagnostic strategies and how to apply them correctly; to have a solid knowledge of the most technological innovations for preventive and containment defense from phytosanitary adversities, as described in the course.
ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING
Have understood how the management of phytosanitary problems must be carried out through digital and innovative approaches, such as pre- and post-onset strategies must be implemented to minimize phytopathological damage.
AUTONOMY OF JUDGMENT
Be able to face a phytopathology with the methodologies discussed in class or similar to them and show to be able to draw on the knowledge acquired in the course to better manage these issues.
CONDITIONS OF THE EXAMINATION:
• The Final Oral Exam focuses mainly on the topics of the Course Program and the knowledge acquired during seminars and exercises. To this the discussion of a topical topic assigned by the teacher at the end of the lessons can be added.
• If during the course of the Final Oral Exam cognitive gaps emerge on the part concerning the basics of plant pathology, a fundamental prerequisite for access to the course, the teacher reserves the right to deepen the assessment of the knowledge of these topics by the students, and to partially take them into account in the final score.
• The final score is made up of 90% of the outcome of the oral exam and 10% of the student’s teacher’s assessment of: active participation during the course and related activities; modalities of expression and mastery of the correct terminology; critical vision of the opportunities offered by technological innovations to address phytopathological problems; global mastery of matter (link between different topics).
• The calendar of exam session and the registration for exam is made through the University portal GOMP.
• Each student has the right to take the exam no more than 3 times per year (academic).
books
On the MOODLE portal the PowerPoint presentations of the lessons are made available, with graphic illustrations, photographs, videos and animations.
It also offers in-depth studies and examples related to some lessons, selection of scientific bibliography on the subject, and a forum for the exchange of views and information with the teacher.
mode
Frontal lessons in the classroom, presentations in PowerPoint with graphic illustrations, photos, video and animations. On Google Classroom will be offered: insights and examples of specific topics related to lectures, a selection of related scientific literature, exchange of information.
Practical lessons and laboratory training are also scheduled
classRoomMode
Although the attendance at the lessons of the Course in question is optional, a regular participation is strongly recommended.
bibliography
A selection of scientific bibliography on the subject is offered by the teacher.
120463 - .
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13
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Learning objectives
The objectives of the Precision Agronomic Techniques course are to provide students with the ability to use digital tools and technologies for the monitoring, analysis and management of cropping systems and for the application of precision agronomic techniques for open field applications with particular regard to herbaceous cropping systems. Attendance at lectures and exercises, although optional, is strongly recommended.
Knowledge and understanding
The course aims to develop in students knowledge and understanding skills, such as:
• know and understand which technologies are useful for monitoring cropping systems for precision agronomic applications such as multispectral and hyperspectral remote sensing to quantitatively estimate variables of agronomic interest of vegetation and soil;
• to know and understand the techniques and technologies that can be used to analyze the spatial and temporal variability of cultivated plots, in particular by exploiting process-based agronomic modeling tools;
• know and understand the methods of development and application of precision agronomic techniques such as seeding, fertilization and irrigation.
Applied knowledge and understanding
The course will allow students to apply knowledge and understanding, allowing for example to:
• know and use the main multispectral satellite systems suitable for precision agriculture through the use of cloud-based platforms for the analysis of the temporal and spatial variability of cultivated plots;
• know and use the techniques to estimate biophysical variables of vegetation and soil from satellite data for the purpose of monitoring agricultural crops;
• know and use a proces-based agronomic model to analyze agronomic management scenarios;
• know the techniques and technologies and equipment for precision seeding, irrigation and fertilization.
Making judgements
The course will allow students to develop autonomy of judgment at various levels, such as:
• hypothesize which properties of the soil and atmosphere influence the spatial and temporal variability of agricultural production;
• propose the most suitable precision management agrotechniques for efficient and sustainable management of herbaceous crops.
Communication skills
Participating in the lessons and/or using the material made available independently will facilitate the development and application of communication skills, such as:
• provide a sufficient range of practical examples of the application of precision agronomic techniques to herbaceous crops;
• use an appropriate and up-to-date agronomic technical vocabulary.
Learning skills
Participating in the lessons and/or independently using the material made available will facilitate the consolidation of one's learning skills, allowing for example to:
• activate a program of continuous education updating of one's knowledge;
• Independently identify the ways to acquire information;
• identify and use the sources of information most useful to staff updating.
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VALERIO CRISTOFORI
First Semester
6
AGR/03
Learning objectives
The learning objectives of teaching Digital Applications in foothill arboriculture are to provide the student with the ability to use digital tools and technologies for monitoring analysis and management of fruit tree systems and for the application of precision agronomic techniques in the field with regard to fruit trees from the foothill environment.
The course also intends to provide students with the ability to identify the most appropriate level of digitization applicable to the different types of orchard farms, together with an in-depth exploration of the different plant shapes used in fruit tree systems, with the aim of calibrating the applications of fruit farming 4.0 to the type of planting and plant shapes used in the orchard. The objectives described above are also pursued through the exploration of appropriate case studies.
Knowledge and understanding skills
The teaching aims to develop students' knowledge and understanding skills, such as:
• knowing and understanding what technologies are useful in monitoring tree systems for precision agronomic applications such as remote sensing and digital soil mapping to quantitatively estimate variables of agronomic interest in vegetation and soil;
• know and understand the digital techniques and technologies that can be used to analyze the spatial and temporal variability of the orchard;
• to know and understand the development and application of precision agronomic techniques and decision support systems for plant fruit systems.
Applied knowledge and understanding
The teaching will enable the application of knowledge and understanding, allowing the student to:
• know and use the main multispectral satellite systems suitable for precision agriculture through the use of cloud-based platforms for analyzing the temporal and spatial variability of fruit-growing plots;
• know and use techniques for estimating vegetation and soil biophysical variables from satellite data and through the use of proximal sensing for monitoring fruit crops;
• to know the techniques and technologies available for digital applications in the management of cultivation operations in the orchard, also exploring the opportunities for using drones and agribots for the automatic execution of cultivation operations.
Autonomy of judgement
Teaching will allow the development of autonomy of judgement at various levels, such as:
• hypothesize which soil and climate properties influence the spatial and temporal variability of fruit tree crops;
• propose the most suitable precision management agro-techniques for efficient and sustainable management of fruit tree crops.
Communication skills
Participation in the lectures and use of the teaching materials made available will facilitate the development and application of communication skills, such as:
• provide an exhaustive range of practical examples of the application of precision agronomic techniques to fruit tree crops;
• using an appropriate and up-to-date technical agronomic vocabulary in line with fruit growing 4.0.
Learning skills
Participating in lessons and making independent use of the material made available will facilitate the consolidation of one's learning skills, such as:
• activate a programme of continuous updating of one's knowledge;
• autonomously identify ways of acquiring information by consulting bibliographic databases at various levels (peer-reviewed journals, popular journals, conference proceedings, websites, etc.);
• identify and use the most useful sources of information for personal updating.
Part 1. Suitability of the orchard for Precision Farming applications and ecophysiological monitoring of the fruit plant
The course deals with fruit tree water relations and interaction with soil and environment. Relationships of fruit trees with light: effects of cultivation practices on plant-light interactions. Gas exchange of fruit trees: photosynthesis/transpiration parameters; effects of environment and soil on photosynthesis and tree productivity. Fruit tree architecture and orchard design for precision farming applications. Fruit development and ripening: effects of cultivation technique and environment on fruit growth and ripening. fruit growth models and measurement methods using field sensors.
Part 2. Forecasting models and sensor technology for monitoring the state of the orchard
The course covers the type and use of traditional and innovative tools and sensor technology to measure crop, environmental and soil variables. Analytical approaches to orchard monitoring and management and artificial intelligence models. Data processing and integration of derived information into farm management and decision support information systems (DSS). Definition of prescription maps and use of UAV (unmanned aerial vehicle) and UGV (unmanned ground vehicle) in the orchard system.
Part 3: Case studies
Field experiments and case studies with the aim of gaining first-hand experience of current precision orchard management technologies available on the market.
examMode
Oral interview on the topics dealt with in the classroom and during the exercises. Recognition of fruit species through plant finds.
"In the evaluation of the evidence (or evidence) in the attribution of the final grade will be taken into account: the level of knowledge of the content demonstrated (superficial, appropriate, precise and complete, complete and thorough), the ability to apply theoretical concepts (discrete, good, well established), the ability to analysis, synthesis and interdisciplinary links (sufficient, good, excellent), the ability to sense criticism and formulating judgments (sufficient, good, excellent), the mastery of expression (exposure deficient, simple, clear and correct, safe and correct).
books
- Casa Raffaele (editore) 2016. Agricoltura di precisione: metodi e tecnologie per migliorare l’efficienza e la sostenibilità dei sistemi colturali. Edagricole New Business Media
- Gentile Alessandra, Inglese Paolo, Tagliavini Massimo (editori) 2022. Arboricoltura Speciale. Edagricole New Business Media
Lecture notes, handouts and articles provided by the instructor through internet services managed by UNITUS
mode
Classroom lessons (40 hours), field and laboratory exercises (8 hours).
Possibility of distance teaching through live and recorded videoconferences.
classRoomMode
Optional attendance
bibliography
- Journal of Fruit Growing and Horticulture ((https://rivistafrutticoltura.edagricole.it)
- The Agricultural Informer (https://www.informatoreagrario.it)
120464 - .
LUCIANO ORTENZI
First Semester
8
INF/01
Learning objectives
The objectives of the Artificial Intelligence Applications course are to provide students with the ability to use advanced statistical tools such as machine learning to understand, design and solve problems concerning the estimation of quantitative or qualitative variables.
Attendance at lessons and exercises, although optional is strongly recommended.
Knowledge and understanding
The course aims to develop in students knowledge and understanding skills, such as:
• know and understand what a machine learning problem is and when to use machine learning to solve a problem;
• know and understand the logic behind machine learning and the most common machine learning techniques;
• know and understand how to develop simple machine learning models and their training.
Applied knowledge and understanding
The course will allow students to apply knowledge and understanding, allowing for example to:
• divide problems into general categories;
• match problems with the most suitable algorithms to solve them;
• design and train machine learning algorithms that can estimate qualitative or quantitative variables based on structured and non-structured datasets.
Making judgements
The course will allow students to develop autonomy of judgment at various levels, such as:
• identify possible sources of uncertainty in the estimation of variables by machine learning (underfitting, overfitting, etc.);
• propose critical solutions to correct trends that undermine the value of the estimate.
Communication skills
Participating in the lessons and/or using the material made available independently will facilitate the development and application of communication skills, such as:
• provide a sufficient range of practical examples of application of artificial intelligence;
• use a suitable and up-to-date computer science technical vocabulary.
Learning skills
Participating in the lessons and/or independently using the material made available will facilitate the consolidation of one's learning skills, allowing for example to:
• activate a program of continuous education updating of one's knowledge;
• independently identify the ways to acquire information;
• identify and use the sources of information most useful to staff updating.
SUPERVISED LEARNING
Introduction, what is machine learning: definitions,
concepts and applications, coding (basic knowledge).
Linear regression model (Cost function,gradient descent,
learning rate, pseudoinverse matrix formula)
Multiple features (gradient descent for multiple linear regression)
Feature scaling and Z−score,Feature engineering,Polynomial regression
Logistic regression, Decision boundary (cost function for logistic
regression gradient descent implementation). The problem of overfitting
regularization for linear regression and logistic regression
2 UNSUPERVISED LEARNING
The clustering problem, the K-means algorithm, Optimization objective
kNN algorithm, Anomaly detection algorithm
Anomaly detection vs. supervised learning
3 MACHINE LEARNING IN PRACTICE
Hyperparameters, and training strategies. Model evaluation model
selection, overfitting, underfitting and regularization
Baseline level of performance and learning curves
Error analysis and iterative loop of ML development
Transfer learning: using data from a different task,error metrics
for skewed datasets, Trading off precision and recall
4 NEURAL NETWORKS AND DEEP LEARNING
TensorFlow and Matlab implementation
Training Details Activation functions (sigmoid, ReLu, etc)
Multiclass classification and Softmax and advanced implementations
The course includes some intermediate tests and a final evaluation based on a machine learning problem.
books
- Abhishek Kumar Pandey, Pramod Singh Rathore, Dr. S. Balamurugan "A Practical Approach for Machine Learning and Deep Learning Algorithms Tools and Technique using MATLAB and Python", BPB Publications, INDIA ISBN: 978-93-88511-13-1
- Aurélien Géron, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Concepts, Tools, and Techniques to Build Intelligent Systems)", O'REILLY
- Ian Goodfellow_Yoshua Bengio_ Aaron Courville - Deep Learning (2016_ The MIT Press)
classRoomMode
nessuna
bibliography
- Abhishek Kumar Pandey, Pramod Singh Rathore, Dr. S. Balamurugan "A Practical Approach for Machine Learning and Deep Learning Algorithms Tools and Technique using MATLAB and Python", BPB Publications, INDIA ISBN: 978-93-88511-13-1
- Aurélien Géron, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Concepts, Tools, and Techniques to Build Intelligent Systems)", O'REILLY
- Ian Goodfellow_Yoshua Bengio_ Aaron Courville - Deep Learning (2016_ The MIT Press)
119427 - ADVANCED ENGLISH (C1)
Second Semester
3
L-LIN/12
Learning objectives
Learning objectives
The minimum educational objectives of the course are aimed at enabling the student to effectively read and understand (reading-comprehension) texts in English such as scientific and/or popular articles, book chapters, etc., as well as to communicate with foreigners and dialogue, with particular reference to the contents of the master's degree course, with foreign interlocutors.
Knowledge and understanding
The student must demonstrate that he/she has acquired a level of knowledge and understanding of linguistic contents (reading, understanding and analysis of scientific texts, dialogue) of C1 level.
Applied knowledge and understanding
The student must demonstrate that he/she is able to apply the knowledge acquired and the understanding of the educational contents provided by confidently passing the final assessment test.
Autonomy of judgment
The student must demonstrate that he/she is able to critically and independently analyze the available teaching material, and also propose autonomous self-learning activities.
Communication skills
During the course, students must demonstrate good oral communication skills in English.
Learning skills
The student must demonstrate an ability to learn the teaching content at a level at least equal to C1.
119426 -
Second Semester
8
119515 - DRONES AND LAND SURVEY
STEFANO BIGIOTTI
Second Semester
6
AGR/10
Learning objectives
Knowledge and Understanding
The course aims to provide students with the necessary knowledge to carry out a topographic survey using the most modern techniques: GPS/GNSS and Remotely Piloted Aircraft Systems (RPAS). The goal is to enable the acquisition of precise knowledge regarding both aerial and terrestrial unmanned surveying systems, applicable to individual and environmental surveying in the field of animal husbandry. Additionally, the course aims to ensure knowledge of the subject from the perspective of usage methods and directly applicable applications. Specifically, the satellite constellation, control systems, and ground user segments will be analyzed. The course will also cover the digital processing and representation of data acquired through surveying activities, with an in-depth focus on the software and processing techniques involved.
Applied Knowledge and Understanding
The course intends to help students acquire the knowledge and skills needed to implement and utilize aerial and terrestrial unmanned surveying systems in the agricultural sector and mountainous terrain. These systems have various applications, including individual and environmental surveying in animal husbandry.
Additionally, the course aims to promote the use of GIS tools and the application of global satellite positioning systems, satellite remote sensing, and the main types of ground receivers.
Autonomy in Judgment
The course also aims to ensure that students understand digital technologies and can apply them in various contexts, including business and regional levels, with particular reference to mountainous areas. It also fosters the acquisition of the necessary skills to communicate relevant information to other engineering professionals working in the field, aiding in the design of technologies related to surveying systems. This includes promoting the development of independent judgment through the cultivation of critical skills aimed at identifying technical and scientific issues related to the subject, evaluating complex surveying projects and flight plans, conducting bibliographic research on scientific, regulatory, and technical sources, and delving into social, professional, and ethical considerations associated with surveying activities. The course will thus address aspects related to the knowledge and use of surveying with RPAS (Remotely Piloted Aircraft Systems), focusing particularly on the regulatory framework, types of RPAS, and the planning of photogrammetric flights.
Communication Skills
The course also aims to enable students to develop specific skills through educational activities to ensure an adequate level of communication regarding ideas, problems, and solutions related to the technical and scientific training pertinent to digital surveying issues.
Learning skills
The course is also designed to help students develop the technological skills needed to ensure continuous updating of knowledge relevant to their professional or scientific activities. This involves consulting regulatory, legislative, technological, digital, methodological, and experimental innovation sources related to current surveying systems. After revisiting the basic concepts of topographic surveying, students will be provided with the necessary knowledge to ensure the correct use of the global positioning system, fostering an understanding of geostatistics, global satellite positioning systems, satellite remote sensing, and the main types of ground receivers.
The exam program is divided into three modules, each of which takes up approximately 33% of the available lesson hours. These modules can be summarized as follows:
1) Recalls of territory surveying: angle, angle measurement systems, angular conversions, distance, altitude, elevation difference, slope, reference systems, geographic coordinates, Cartesian coordinates.
2) GPS/GNSS positioning, the space, control, and user segments. Types of surveying, errors, and modeling. Networks of permanent GNSS stations. Evaluation of achievable accuracies with different GNSS positioning techniques and comparison with traditional techniques. Applications of use and integration with other surveying methodologies.
3) Surveying of paths and equipped areas using RPAS (Remotely Piloted Aircraft Systems)
- Types of RPAS: multirotors, fixed-wing, hybrid drones, regulatory and legislative framework;
- Orientation parameters of the frames. Digital photogrammetry, image acquisition;
- Parameters and planning of aerial photogrammetric flight, arrangement of ground control points (GCP);
The third module will also include practical exercises aimed at improving the student's skills in surveying activities through hands-on experiences with technologies related to the topic of RPAS.
examMode
The evaluation method consists of an oral exam, conducted through a series of questions designed to assess the student's theoretical knowledge of the topics covered during the course, ensuring that the level of critical awareness developed regarding the main issues addressed is also examined.
The final exam consists of an oral test aimed at evaluating the competencies acquired in the subject and the critical interpretation skills developed by the student during the course. In particular, the oral exam will focus on the topics relevant to the three modules outlined in the program, consisting of three questions, each pertaining to a section of the course, including references to sector regulations.
During the course, students will have the opportunity to take partial in-progress tests. This test, lasting 1 hour and 30 minutes, will consist of three open-ended questions and will cover the part of the program related to GPS systems.
The evaluation will be expressed in a grade out of thirty.
books
Lecture notes prepared by the instructor. The material will be made available to students through the Moodle platform.
mode
The course will be conducted in person; however, if necessary, students will still have the option to connect remotely to attend the lectures.
classRoomMode
Attendance to the course is not mandatory but optional.
bibliography
Lecture notes prepared by the instructor. The material will be made available to students through the Moodle platform.
120425 - .
VALENTINA BIGINI
Second Semester
6
BIO/04
Learning objectives
Knowledge and ability to understand
The course aims to consolidate and expand the knowledge of the biology of plant organisms, with regard to ecophysiological aspects. Students will learn, in class and with originality, multidisciplinary approaches more related to genetics, molecular biology, biochemistry and plant physiology.
Applying knowledge and understanding
Students will acquire the ability to independently solve problems related to crop resilience, critically analysing the biochemical and physiological mechanisms that plants put in place to adapt to unfavourable environmental conditions and to defend themselves from pathogens.
Making judgement
Students will develop the ability to synthesize and integrate knowledge by making solid judgments.
Communication skills
Conclusions and recommendations will be communicated by students through the argumentation of the knowledge gained during the course and the motivations behind it, both to a specialized and non-specialist audience, in a clear and unambiguous way.
Learning skills
The notions and concepts acquired during the course will provide students with greater responsibility for further professional development.
The objectives of the Precision Agronomic Techniques course are to provide students with the ability to use digital tools and technologies for the monitoring, analysis and management of cropping systems and for the application of precision agronomic techniques for open field applications with particular regard to herbaceous cropping systems. Attendance at lectures and exercises, although optional, is strongly recommended.
Knowledge and understanding
The course aims to develop in students knowledge and understanding skills, such as:
• know and understand which technologies are useful for monitoring cropping systems for precision agronomic applications such as multispectral and hyperspectral remote sensing to quantitatively estimate variables of agronomic interest of vegetation and soil;
• to know and understand the techniques and technologies that can be used to analyze the spatial and temporal variability of cultivated plots, in particular by exploiting process-based agronomic modeling tools;
• know and understand the methods of development and application of precision agronomic techniques such as seeding, fertilization and irrigation.
Applied knowledge and understanding
The course will allow students to apply knowledge and understanding, allowing for example to:
• know and use the main multispectral satellite systems suitable for precision agriculture through the use of cloud-based platforms for the analysis of the temporal and spatial variability of cultivated plots;
• know and use the techniques to estimate biophysical variables of vegetation and soil from satellite data for the purpose of monitoring agricultural crops;
• know and use a proces-based agronomic model to analyze agronomic management scenarios;
• know the techniques and technologies and equipment for precision seeding, irrigation and fertilization.
Making judgements
The course will allow students to develop autonomy of judgment at various levels, such as:
• hypothesize which properties of the soil and atmosphere influence the spatial and temporal variability of agricultural production;
• propose the most suitable precision management agrotechniques for efficient and sustainable management of herbaceous crops.
Communication skills
Participating in the lessons and/or using the material made available independently will facilitate the development and application of communication skills, such as:
• provide a sufficient range of practical examples of the application of precision agronomic techniques to herbaceous crops;
• use an appropriate and up-to-date agronomic technical vocabulary.
Learning skills
Participating in the lessons and/or independently using the material made available will facilitate the consolidation of one's learning skills, allowing for example to:
• activate a program of continuous education updating of one's knowledge;
• Independently identify the ways to acquire information;
• identify and use the sources of information most useful to staff updating.
.
RAFFAELE CASA
First Semester
7
AGR/02
Learning objectives
The objectives of the Precision Agronomic Techniques course are to provide students with the ability to use digital tools and technologies for the monitoring, analysis and management of cropping systems and for the application of precision agronomic techniques for open field applications with particular regard to herbaceous cropping systems. Attendance at lectures and exercises, although optional, is strongly recommended.
Knowledge and understanding
The course aims to develop in students knowledge and understanding skills, such as:
• know and understand which technologies are useful for monitoring cropping systems for precision agronomic applications such as multispectral and hyperspectral remote sensing to quantitatively estimate variables of agronomic interest of vegetation and soil;
• to know and understand the techniques and technologies that can be used to analyze the spatial and temporal variability of cultivated plots, in particular by exploiting process-based agronomic modeling tools;
• know and understand the methods of development and application of precision agronomic techniques such as seeding, fertilization and irrigation.
Applied knowledge and understanding
The course will allow students to apply knowledge and understanding, allowing for example to:
• know and use the main multispectral satellite systems suitable for precision agriculture through the use of cloud-based platforms for the analysis of the temporal and spatial variability of cultivated plots;
• know and use the techniques to estimate biophysical variables of vegetation and soil from satellite data for the purpose of monitoring agricultural crops;
• know and use a proces-based agronomic model to analyze agronomic management scenarios;
• know the techniques and technologies and equipment for precision seeding, irrigation and fertilization.
Making judgements
The course will allow students to develop autonomy of judgment at various levels, such as:
• hypothesize which properties of the soil and atmosphere influence the spatial and temporal variability of agricultural production;
• propose the most suitable precision management agrotechniques for efficient and sustainable management of herbaceous crops.
Communication skills
Participating in the lessons and/or using the material made available independently will facilitate the development and application of communication skills, such as:
• provide a sufficient range of practical examples of the application of precision agronomic techniques to herbaceous crops;
• use an appropriate and up-to-date agronomic technical vocabulary.
Learning skills
Participating in the lessons and/or independently using the material made available will facilitate the consolidation of one's learning skills, allowing for example to:
• activate a program of continuous education updating of one's knowledge;
• Independently identify the ways to acquire information;
• identify and use the sources of information most useful to staff updating.
Part 1. Monitoring tools of cropping systems for precision agronomic applications.
Remote sensing to support precision agronomic management. Multispectral and hyperspectral satellite platforms suitable for agronomic applications of precision agriculture. Application of remote sensing to the monitoring of agricultural crops. Qualitative and quantitative approaches for the estimation of biophysical variables of agricultural crops and soil. Radiative transfer models. The problem of modeling inversion, hybrid methods. Applications of remote sensing to agricultural soil monitoring on a farm and field scale. Sensors and methods for proximal surveys of vegetation properties.
Part 2. Cropping systems analysis tools for precision and digital farming applications.
Introduction to spatial data analysis methods. Introduction to geostatistics. Definition of zoning into homogeneous areas from an agronomic point of view. Zoning methods. Basic concepts for the preparation of prescription maps of agronomic practices.
Simulation models and decision support systems in precision agriculture. Simulation modelling: the crop. Motivation and basic concepts; simulation of phenological development; simulation of biomass growth; tools currently available. Simulation modelling: the soil. Movement of water in the soil; nitrogen availability and greenhouse gas emissions; use cases.
Decision Support Systems (DSS): agronomic applications and case studies.
Part 3. Precision agronomic practices.
Soil tillage: generalities, definitions, equipment, tillage techniques, use of precision systems in soil tillage, examples of variable intensity soil tillage based on maps and based on sensors.
Sowing: classification and operation of seeders, parameters to be considered for quality sowing, map-based variable dose sowing, adjustment of seeders in variable mode.
Precision fertilization. General concepts for precision fertilization. Nitrogen fertilization. Phosphate fertilization. Potassium fertilization. Organic fertilization. The correction of pH. Variable rate fertilization equipment in precision agriculture.
Precision irrigation. Decision-making, zoning for precision irrigation. Support systems (DSS) for irrigation. Precision irrigation techniques and systems.
Precision agriculture for herbaceous crops: case studies.
Exercises in the laboratory (computer) and in the field.
examMode
The examination will take place through general questions on each of the three different parts of the programme.
books
R.Casa (ed.) 2024. Agricoltura di precisione: metodi e tecnologie per migliorare l’efficienza e la sostenibilità dei sistemi colturali. 2a Edizione. Edagricole New Business Media (https://www.tecnichenuove.com/libri/agricoltura-di-precisione)
Slides and material distributed by the teacher.
mode
Lectures in the classroom, with simultaneous streaming via Zoom if allowed by university regulations and infrastructures
Exercises with computers and in the field
classRoomMode
Attendance, although not mandatory, is essential to achieve the indicated training objectives, especially with regard to computer exercises.
bibliography
R.Casa (ed.) 2024. Agricoltura di precisione: metodi e tecnologie per migliorare l’efficienza e la sostenibilità dei sistemi colturali. 2a Edizione. Edagricole New Business Media (https://www.tecnichenuove.com/libri/agricoltura-di-precisione)
Slides and material distributed by the teacher.
NEW EXTRA CURRICULAR GROUP
-
-
-
-
.
MASSIMO CECCHINI
First Semester
4
AGR/09
.
LEONARDO BIANCHINI
First Semester
3
AGR/09
SUBJECT
SEMESTER
CFU
SSD
LANGUAGE
119416 - DIGITAL TECHNOLOGIES APPLIED TO GENETICS
MARIO AUGUSTO PAGNOTTA
First Semester
6
AGR/07
Learning objectives
Knowledge and understanding
The course aims to provide the necessary knowledge for the evaluation of phenotypes and their genetic bases in order to learn the body's responses to different environmental situation and to be able to favor those most suited to specific needs. The basics of modern genetic analysis from sequencing to the evaluation of genomes and biodiversity will also be provided.
Applied knowledge and understanding
The course deals with genotypic and genomic characterization (morpho-bio-molecular markers; automation in field genotyping - NGS, DNA barcoding, genotyping by sequencing; population genetics; management of natural populations), phenotypic characterization (tolerance traits abiotic stress observation and parameterization; phenotyping of the individual, populations and communities; analysis of point and area data, from multispectral analysis to phenotype), from genotype to phenotype (gene regulation; phenotypic plasticity; epi-genetics), the exploitation of germplasm (characterization, enhancement and conservation of germplasm; general principles and application to case studies).
Making judgments
Know how to decide the best genetic evaluation and biodiversity conservation methodologies to use in different situations.
Communication skills
Acquire technical terminology to communicate information, ideas, problems and solutions clearly and in detail to the scientific and public community.
Learning skills
Develop learning skills necessary to undertake further studies with a high degree of autonomy.
The course deals with genotypic and genomic characterization (morpho-bio-molecular markers; automation in field genotyping - NGS, DNA barcoding, genotyping by sequencing; population genetics; management of natural populations), phenotypic characterization (tolerance traits abiotic stress observation and parameterization; phenotyping of the individual, populations and communities; analysis of point and area data, from multispectral analysis to phenotype), from genotype to phenotype (gene regulation; phenotypic plasticity; epi-genetics), the exploitation of germplasm (characterization, enhancement and conservation of germplasm; general principles and application to case studies).
examMode
It will be verified that the expected learning outcomes are acquired by the students. The exhibition capacity, completeness and detail of the individual topics requested will be assessed. The ability to link the different topics will also be considered. For the attribution of the final mark, account will be taken of: the level of knowledge of the contents shown (superficial, appropriate, precise and complete, complete and thorough), the ability to analyze, summarize and interdisciplinary links (sufficient, good, excellent), the capacity for critical sense and the formulation of judgments (sufficient, good, excellent), the mastery of expression (poor, simple, clear and correct, safe and correct exposition). In particular, the final judgment and grade will consider the knowledge and concepts acquired, the ability to analyze problems, to connect interdisciplinary knowledge, to formulate hypotheses and judgments, to master and clarity of expression and exposure.
books
Genetica. Un approccio molecolare. Ediz. MyLab. di Peter J. Russell (Autore), Carla Cicchini (a cura di), Alessandra Marchetti (a cura di) Pearson Ed ISBN 8891906964
Genetica molecolare. Biologia molecolare del gene di L. Sanguini (Autore), M. Cerofolini (Autore). Edizioni Esagono. ISBN 8843360159
Genetica e biologia molecolare di Peter H. Raven (Autore), G. B. Johnson (Autore), K. A. Mason (Autore), Jonathan B. Losos (Autore), S. R. Singer (Autore). PICCIN ED ISBN 8829929522
Dispense
mode
Lectures, classroom exercises, laboratory and field exercises.
classRoomMode
Presence + on-line
bibliography
Genetica. Un approccio molecolare. Ediz. MyLab. di Peter J. Russell (Autore), Carla Cicchini (a cura di), Alessandra Marchetti (a cura di) Pearson Ed ISBN 8891906964
Genetica molecolare. Biologia molecolare del gene di L. Sanguini (Autore), M. Cerofolini (Autore). Edizioni Esagono. ISBN 8843360159
Genetica e biologia molecolare di Peter H. Raven (Autore), G. B. Johnson (Autore), K. A. Mason (Autore), Jonathan B. Losos (Autore), S. R. Singer (Autore). PICCIN ED ISBN 8829929522
119485 - DIGITAL MAPPING OF SOIL AND TERRITORY
-
12
-
-
Learning objectives
The main objective of the course is to provide knowledge of the methods and tools for observing and analyzing the territory, offering advanced insights into Geographic Information Systems (GIS), Remote Sensing, and spatial analysis of territorial data.
Knowledge and understanding
The student will acquire specific skills related to the acquisition of georeferenced data available from major databases (such as the National Geoportal, ISTAT database, Copernicus, Regional Web GIS, etc.), the analysis and processing of such data, and the production of georeferenced data through monitoring or derived from spatial analyses. Whenever possible, students will be involved in activities related to ongoing research projects.
Applying knowledge and understanding
By the end of the course, the student will be familiar with the fundamental elements of cartography and digital cartographic representation. They will be able to create thematic maps related to territorial elements, conduct spatial analyses of various phenomena, and develop a cartographic project. The student will have gained proficiency in using GIS software and employing remotely sensed images for territorial analyses.
Making judgements The course aims to develop analytical skills at the territorial scale with the goal of proposing technical and practical solutions
Communication skills
The student will be required to produce an exam work by applying the acquired knowledge, conducting part of the work independently and part in a group to promote learning ability and work autonomy.
Learning skills
During the course, the student will be able to develop learning skills through active participation. Throughout the lessons, the student will have the opportunity to identify methods for acquiring and updating information, select and utilize the most useful sources, apply the acquired knowledge, and assess their own level of learning.
MODULE II
MARIA NICOLINA RIPA
Second Semester
6
AGR/10
Learning objectives
The main objective of the course is to provide knowledge of the methods and tools for observing and analyzing the territory, offering advanced insights into Geographic Information Systems (GIS), Remote Sensing, and spatial analysis of territorial data.
Knowledge and understanding
The student will acquire specific skills related to the acquisition of georeferenced data available from major databases (such as the National Geoportal, ISTAT database, Copernicus, Regional Web GIS, etc.), the analysis and processing of such data, and the production of georeferenced data through monitoring or derived from spatial analyses. Whenever possible, students will be involved in activities related to ongoing research projects.
Applying knowledge and understanding
By the end of the course, the student will be familiar with the fundamental elements of cartography and digital cartographic representation. They will be able to create thematic maps related to territorial elements, conduct spatial analyses of various phenomena, and develop a cartographic project. The student will have gained proficiency in using GIS software and employing remotely sensed images for territorial analyses.
Making judgements The course aims to develop analytical skills at the territorial scale with the goal of proposing technical and practical solutions
Communication skills
The student will be required to produce an exam work by applying the acquired knowledge, conducting part of the work independently and part in a group to promote learning ability and work autonomy.
Learning skills
During the course, the student will be able to develop learning skills through active participation. Throughout the lessons, the student will have the opportunity to identify methods for acquiring and updating information, select and utilize the most useful sources, apply the acquired knowledge, and assess their own level of learning.
Teaching is divided into a theoretical part and an applied part aimed at developing a cartographic project work.
Basic elements of classical cartography (Definitions, properties, scale, projections, reference systems, symbology, types of maps, etc.).
Basic elements of numerical cartography (Structure of GIS, geographic data representation models, geodatabases)
Forms of cartographic representation (cartograms with GIS and overlaying operations: exercises
Acquisition of spatial data: a. Basic functions (digitization and photointerpretation, georeferencing operations); b. Advanced functions (spatialization and interpolation operations, remote sensing images)
Cartographic representation as a form of visual communication and decision support
Analysis and processing of spatial data. Insights into specific topics and procedures for carrying out project work to be defined annually.
examMode
The exam consists of:
- Performing exercises in class
During the exam, students will execute GIS exercises.
Assessment considers the level of knowledge of the content, expression ability, capacity to apply acquired knowledge critically, and interdisciplinary connections.
books
Notes, scientific articles and educational material available on the moodle platform
Pesaresi C., (2017) GIS applications. Methodological principles and lines of research. Exercises and guiding examples. UTET
mode
The class consists of lectures and of practical GIS application during which students acquires skills related to the representation of the territory and the use of GIS for spatial analysis following a guided learning path. The class is in the Geomatics room that is equipped with the QGIS software, an open source software that students can freely install on their PC in order to operate independently to complete the project work to be discussed during the exam
classRoomMode
Attendance is not mandatory but it is strongly recommended
bibliography
Notes, scientific articles and educational material available on the moodle platform
MODULE II
SIMONE PRIORI
Second Semester
6
AGR/14
Learning objectives
The main objective of the teaching is to provide the knowledge required to understand the characteristics and spatial variability of soils, for proper site-specific soil management in agriculture and agro-ecosystem. Basic concepts of soil chemistry, physics and hydrology, pedogenetic factors and processes will be recalled. The student will learn to frame soil variability within an agro-ecosystem landscape, learn digital soil mapping techniques using GIS software and the use of innovative techniques for soil monitoring and mapping, in particular the use of proximal sensors such as electromagnetic induction and diffuse reflectance spectrometry. The student will also learn the applications of mapping products and soil data, such as land suitability maps, monitoring soil functionality, etc.
Knowledge and ability to understand
The student will have to demonstrate that he/she has learnt and understood the main aspects of soil mapping and monitoring, namely:
• the main chemical, physical and hydrological characteristics of soils;
• the principles of horizon and soil classification;
• the principles of soil mapping, especially digital mapping, using methods of data spatialization and clustering of homogeneous units through GIS software;
• the principles of soil science applied to agronomy with regard to soil suitability, water and nutrient availability, recognition of possible problems (e.g. waterlogging, erosion susceptibility, etc.).
Applying knowledge and understanding
The student will be able to use the acquired knowledge to:
• describe the main characteristics of a soil profile and the associated pedogenetic processes, understanding the links between environmental characteristics and the chemical-physical and hydrological ones;
• understand the location of a certain soil type within a landscape and its geographical limits related to variations in pedogenetic factors;
• apply proximal soil sensing techniques using sensors and carry out the spatialization of soil data;
• be able to identify any problems or risks related to soil functionality and circumscribe them.
Making judgement
The student must be able to independently recognise a certain soil type and the soil processes present. He/she must know how to set up a soil survey and a description of a soil profile or soil borehole, as well as interpret a soil map or a soil description and analysis. They must also know how to interpret data obtained from proximal geophysical sensors, how to spatialise them in the plot of interest and understand which soil characteristics are associated with the variability of these data.
Communication skills
The student should have the ability to explain in a simple and comprehensive manner the knowledge acquired, trying to connect the basic notions to the more complex topics related to soil mapping and applications of pedology.
Learning ability
The student will have to refer to the teaching program and to the lesson plan of the course, deepening the various topics addressed through the handouts provided by the lecturer, the consultation of recommended texts and publications of national and international relevance.
- Bases of pedology: soil phases, pedogenetic factors and processes, soil profile and genetic horizons
- Elements of soil physics: concepts and methods of measurement of texture, structure, bulk density, compaction, erodibility
- Elements of soil hydrology: water flows in the soil, water retention curves, field capacity, wilting point, available water capacity (AWC), infiltration and permeability of soils, water stagnation and associated pedogenetic forms. Measurement and monitoring of water content and water tension in the field.
- Reading of the pedological landscape: bases of geomorphology, forms of slope and valley deposits, glacial and periglacial forms, karst forms, structural forms. Photointerpretation, digital terrain models. The physiographic units.
- Traditional pedological survey: organization of the survey, description of profiles and drills, chemical-physical parameters to be analyzed, type of survey
- Pedological cartography: Soil-landscape paradigm; hierarchy of pedo-landscapes. Criteria for the definition of cartographic units - The series, the type, the phase, the variants - The composite cartographic units - Associations, complexes - Cartographic units in small-scale surveys. Organization of work for soil survey and mapping
- Geophysical proximal soil sensors: basic geophysical concepts, georesistivimeters and electromagnetic induction sensors. Procedure for proximal detection and data processing.
- Spectrometry: concepts of diffuse reflectance spectrometry in the visible and infrared range. Type of spectrometers, use in the laboratory and in the field. Soil spectrum analysis, construction of a spectral library. Gamma-ray spectrometry and its possible use in agriculture.
- Practical applications of GIS software and geostatistical methods for the processing and mapping of soil data. Clustering and mapping of homogeneous areas for precision agriculture.
examMode
The exam will take place with a practical test of soil data processing and mapping on the PC, using the modalities observed during the course, and an oral test on the topics of the course.
books
Notes provided by the professor
mode
Lezioni frontali ed esercitazioni
classRoomMode
Lectures and exercises on the PC
Practical exercises in the field
bibliography
- IUSS Working Group WRB. 2022. World Reference Base for Soil Resources. International soil classification system for naming soils and creating legends for soil maps. 4th edition. International Union of Soil Sciences (IUSS), Vienna, Austria.
- USDA-Soi Survey Division Staff. Soil Survey Manual. https://www.nrcs.usda.gov/sites/default/files/2022-09/The-Soil-Survey-Manual.pdf
119428 - TRAINING
First Semester
2
119424 - MACHINES AND PLANTS FOR PRECISION FARMING
MASSIMO CECCHINI
Second Semester
6
AGR/09
Learning objectives
The students must acquire basic skills to develop the mechanization of operations in precision farming. In particular, they must be able to choose suitable machines for sustainable and high-quality work (knowing operational methods, safety aspects, etc.) while respecting mechanization constraints (economic, environmental, safety, etc.).
Knowledge and understanding
The student must acquire knowledge and understanding of the principles underlying the design and operation of machines and plants and be able to introduce them into agricultural sites, respecting various constraints.
Applying knowledge and understanding
The student must acquire the ability to apply theoretical knowledge of the topics covered in the course critically to identify individual machines, a fleet of machines, or systems for precision farming.
Making judgements
The student must be able to select specific machines and plants from the market suitable for various types of agricultural work sites where precision farming principles are applied. This should be done objectively, without being influenced by manufacturers, and respecting social, scientific, or ethical aspects related to each mechanization decision.
Communication skills
The student must be able to effectively communicate information about machines and plants and their technical-economic requirements to third parties (employers, clients such as agricultural companies, forestry enterprises, etc.), justifying their choices.
Learning skills
The course structure will be developed to first convey "cross-cutting" basic concepts relevant to any type of machine. Subsequently, individual types of machines (the most widespread in precision farming) will be covered. The topics will be presented to stimulate a desire for learning, logically developing knowledge gradually, from materials and mechanical principles to construction and safety aspects, to machine management. The same logic is required in creating a presentation (flipped classroom), which will be considered in the learning assessment.
Main types of automated machines and systems for the precision agriculture and animal husbandry sector (operating principles, applications, safety and selection criteria).
- Self-driving tractors.
- Variable rate machines.
- Robot for milking and for the preparation and distribution of the diet.
- Drones.
Computing architecture distributed on CAN-ISOBUS machines with virtual terminal.
Safety in the use of machines based on electronic systems (active or passive tags on operators).
Application of augmented reality techniques in the management of machines.
Exercises: 8 hours
2 visits to farms with analysis of the mechanization and safety of machines.
examMode
The oral exam consists of three questions that will cover the entire program of the course. Each answer will be evaluated with a score from 0 to 10. The final mark will be given by the sum of the three individual marks.
For the attribution of the vote, the level of knowledge of the contents demonstrated and the ability to apply the concepts learned will be taken into account. The ability to synthesize and the property of language will also be taken into consideration.
In critical situations, such as a high number of candidates in the booking, or peculiarities of one or more candidates, the exam can be carried out in writing with three open-ended questions. Candidates will be given one and a half hours to respond. Furthermore, at the request of individual students, it is still possible to take the exam in written or oral form, regardless of what is reported in the official session.
In any case, the same evaluation criteria described above will apply.
books
Teaching notes
mode
Lectures in the classroom broadcast in live streaming.
Practice only in presence.
classRoomMode
Attendance at lectures and tutorials is recommended, but not mandatory.
bibliography
Advanced Automation for Tree Fruit Orchards and Vineyards (Ed. 2023)
by Stavros G. Vougioukas (edited by), Qin Zhang (edited by)
Springer International Publishing AG
Precision Farming From Above: How Commercial Drone Systems are Helping Farmers Improve Crop Management, Increase Crop Yields and Create More Profitable Farms. (Ed. 2018)
by Louise Jupp
Writing Matters Publishing
Precision Agriculture: Enabling Technologies (Ed. 2023)
by Nekesah T. Wafullah
Delve Pub
La meccatronica nelle macchine agricole. Dal digitale al Precision Farming (Ed. 2020)
Italian edition by Hanno Speich
Tecniche nuove
Tecnologie di precisione nelle macchine agricole: Telemetria, M2M, IoT, Big Data e Data Science (Ed. 2023)
Italian edition by Rodes Silva
Edizioni Sapienza
Precision farming. Strumenti e tecnologie per un'agricoltura evoluta (Ed. 2020)
Italian edition by Davide Misturini
Edagricole
119417 - DIGITAL MANAGEMENT OF WATER RESOURCES
CIRO APOLLONIO
Second Semester
6
AGR/08
Learning objectives
The course covers the main aspects of digital water resource management at the catchment scale. The course aims to train the learner on the following topics:
• regulatory aspects of water resources management;
• the use of hydrological modelling software;
• the use of hydraulic modelling software to assess the hydraulic characteristics of a free-flowing stream.
Knowledge and understanding
The course aims to develop students' knowledge and understanding skills, such as:
• knowledge and understanding skills in a field of study at a level that is characterised by the use of advanced textbooks and also includes knowledge of some cutting-edge topics in the field of watershed managment;
• ability to understand and hydrological data.
Applied knowledge and understanding
The course will enable them to apply knowledge by demonstrating adequate understanding, enabling them, for example:
• to apply their knowledge and understanding in a way that demonstrates a professional approach to their work, as well as adequate skills to both devise and support arguments to solve problems in the field of watershed managment;
• ability to collect and analyse hydrological data.
Making judgements
The course will allow the development of independent judgement at various levels, such as
• hypothesising which causes most influence the occurrence of hydrogeological instability phenomena using one-dimensional hydraulic modelling software;
• propose solutions for the mitigation of hydrogeological instability phenomena using one-dimensional hydraulic modelling software.
Communication skills
Attending lectures and/or making independent use of the material provided will facilitate the development and application of communication skills, such as:
• ability to communicate information, ideas, problems and solutions, on the topics covered, to specialist and non-specialist people;
• use an appropriate and up-to-date technical vocabulary in the field of hydrological-hydraulic modelling.
Learning skills
Attending lectures and/or making independent use of the material provided will facilitate the consolidation of one's learning skills, enabling one to, for example:
• activate a programme of continuous updating of one's knowledge;
• autonomously identify ways of acquiring information;
• identify and use the most useful sources of information for personal updating.
This learning capacity will be fundamental for undertaking subsequent studies with a high degree of autonomy.
1. Introduction to the course.
a. Recalls of hydrology
b. Recalls of hydraulics
c. Regulatory aspects related to water resources management.
2. Overview of water resources management software: description of CAD software, GIS, hydrologic modeling, hydraulic modeling.
3. Use of hydrological modeling software.
4. The use of hydraulic modeling software for water resources management.
examMode
The exam consists of an oral test on the topics developed during the theoretical and theoretical-practical lessons in the classroom.
books
References:
1- lecturer's notes
2- pdf files of the presented Power Point
3- Ferro V. – La sistemazione dei bacini idrografici, Mc Graw Hill, II ed., 2006.
4- open access software manuals
classRoomMode
Recommended frequency, but not mandatory.
bibliography
Hydrology Handbook, Second Edition, Authored by: Task Committee on Hydrology Handbook of Management Group D of ASCE
119425 - PRECISION LIVESTOCK FARMING
LOREDANA BASIRICO'
Second Semester
6
AGR/18
Learning objectives
In line with the educational objectives of the CdLM in 'Digital Management of Agriculture and Mountain Territory', the teaching provided has the general objective of providing the student with skills on the applications in the livestock sector of the main sensors and tools for precision farming aimed at improving productivity, health and animal welfare and environmental sustainability.
Knowledge and understanding
The student will develop basic and advanced knowledge relating to the possible automation solutions available for the management of animals (ruminants, pigs, poultry), for the control of the microclimate in breeding, for the management of food in breeding and preparation/distribution of the ration, depending on the species bred and the systems for monitoring animal performance and milking automation.
Applying knowledge and understanding
The knowledge acquired will give the student the ability to understand the main critical points related to the management of animals, animal nutrition, and the main digital technological approaches available to improve the production efficiency and sustainability of livestock farming.
Making judgements
The skills and knowledge acquired will allow the student to independently develop their own assessments regarding the resolution of practical problems related to the management of livestock using digital technologies available on the market.
Communication skills
The knowledge acquired by the student will allow him/her to communicate what he/she has learned using appropriate technical and scientific language.
Learning skills
The skills acquired by the student will allow him to develop a critical capacity that will allow him to face with great flexibility the different professional contexts in which he will have to operate.
1. Livestock systems (Knowledge of the main livestock breeding technologies for the production of milk, meat and eggs)
2. Notes on the qualitative aspects of animal production
3. Overview of precision animal husbandry
4. Sensors
5. Precision feeding
6. Precision milking
7. Precision stable management; animal behavior monitoring systems; environmental sensors.
examMode
Oral interview.The assessment test will include at least three questions, which will tend to ascertain the student's theoretical knowledge of the part presented in class. Each question will be evaluated with a score from 0 to 10. The final vote will correspond to the sum of the three single votes. For the purpose of attributing the mark, the commitment and active participation in the exercises proposed during the course will also be taken into consideration. Particular attention is paid to the student's ability to reason across the board, linking the concepts of various parts of the teaching through the necessary logical-deductive connections, and to communicate using clear and appropriate language.
books
SANDRUCCI A., TREVISI E. (A CURA DI), Produzioni Animali. ED. EDISES, 2022.
ABENI F., NANNONI E., SANDRUCCI A. (A CURA DI), Zootecnia di precisione e tecnologie innovative in allevamento. ED. Point Veterinarie Italie (PVI), 2024
Precision technology and sensor applications for livestock farming and companion animals. Edited by E. (Lenny) van Erp-van der Koo, Wageningen Academic Publishers, 2021.
mode
The course is divided into lectures in the classroom, exercises in the classroom or in the laboratory and visits to farms.
1) Lectures to expose the key concepts of the subject. The lessons are accompanied by power point aids, subsequently made available on the Moodle platform;
2) Practical lessons, in the classroom or in the laboratory.
3) Seminars held by recognized experts on specific topics of the course.
classRoomMode
Attendance of classroom lessons is optional, but the participation in exercises and visits to farms are strongly recommended, because they allow the student to learn and appropriate theoretical knowledge in the context of its use.
bibliography
Teaching material provided by the teacher. Lesson notes, reference bibliography or other material will be inserted by the teacher on the dedicated website (Moodle platform).
119429 - FINAL TEST
Second Semester
20
CHOICE GROUPS
YEAR/SEMESTER
CFU
SSD
LANGUAGE
NEW EXTRA CURRICULAR GROUP
-
-
-
120840 - .
MASSIMO CECCHINI
First Year / Second Semester
4
AGR/09
120841 - .
LEONARDO BIANCHINI
First Year / Second Semester
3
AGR/09
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