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Course
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Credits
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Scientific Disciplinary Sector Code
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Contact Hours
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Exercise Hours
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Laboratory Hours
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Personal Study Hours
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Type of Activity
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Optional materials and exam in a foreign language
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Language
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119414 -
Digital techniques in agriculture
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Tecniche agronomiche di precisione
(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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7
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AGR/02
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56
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-
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-
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Core compulsory activities
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ITA |
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119411 -
Informatic Systems
(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.
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Derived from
119411 Sistemi informativi in Gestione digitale dell’agricoltura e del territorio montano LM-69 LM-69 Ortenzi Luciano
( syllabus)
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
Advanced Optimization.
Additional Layer Types Convolutional neural network height.
Deeplearning applications: Image classification and YOLO
( reference 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)
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8
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INF/01
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64
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-
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-
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-
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Core compulsory activities
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ITA |
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119427 -
Advanced English (C1)
(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.
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3
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L-LIN/12
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24
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-
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Other activities
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ITA |
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119426 -
Attività Formativa a Scelta
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8
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64
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Elective activities
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ITA |
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119412 -
Plant ecophysiology
(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.
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Derived from
119412 Ecofisiologia vegetale in Gestione digitale dell’agricoltura e del territorio montano LM-69 LM-69 FORGIONE IVANO
( syllabus)
General traits of the plant cell: structure and function of the cell wall, vacuole and plastids.
Processes of water transport and absorption by the roots. Interaction among root, soil and microbes during the nutrition stages. Plants as phytoremediation.
Stress physiology: drought response mechanisms and plant responses to salt stress; frostbite and heat shock. (these concepts are linked to hormonal responses such as ABA and the efficiency of photosynthesis)
Chemosynthetic and photosynthetic organisms: chemosynthesis and photosynthesis.
Photosynthesis: photosynthetic pigments, excitation of photosynthetic pigments, energy transfer among pigments, photochemistry, photosynthetic transport chain, ATP synthesis, regulation of the light phase, Calvin cycle, structure, catalysis and regulation of ribulose bisphosphate carboxylase / oxygenase. Photorespiration, C4 photosynthesis, CAM plants.
Fate of photosynthates, starch and sucrose. Translocation in the phloem.
Growth and development: embryogenesis, root and shoot development, meristems.
Introduction to the concept of plant hormone: synthesis and functions of hormones.
Auxin: polar transport, cell distension and hypothesis of acid growth; effects on phototropism and gravitropism.
Gibberellins: seeds storage mobilization and relative control of gene expression; stem growth.
Cytokinins: cell division.
Ethylene: fruit ripening.
Abscisic acid: regulation of stomatal openings, leaf senescence.
Responses to red and blue light. Photochemical and biochemical properties of phytochrome. Characteristics of the responses induced by phytochrome. Structure of the phytochrome. Photoreceptors of blue light and photophysiology of blue light responses. Circadian rhythms and flowering regulation (link with some plant hormones).
( reference books)
Fisiologia Vegetale, Taiz- Zeiger, IV° edizione a cura di Massimo Maffei, Piccin
Recent research articles shared during the course.
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6
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AGR/03
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48
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Core compulsory activities
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ITA |
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119515 -
Drones and land survey
(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.
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Derived from
119515 Impiego dei droni e sistemi di rilevamento in Gestione digitale dell’agricoltura e del territorio montano LM-69 LM-69 Marucci Alvaro
( syllabus)
) GPS/GNSS positioning, spatial, control and user segments. Satellite positioning in outdoor sports activities.
2) Basic notions of cartography, angle measurement systems, angular conversions, distance, elevation, slope, reference systems, geographical coordinates, Cartesian coordinates, UTM system, GAUS-BOAGA system, dimensioned plans, contour lines.
3) The survey of the routes and areas equipped using SAPR (Remotely Piloted Aircraft Systems)
- types of UAS: multirotor, fixed wing, hybrid drones, legislative and regulatory reference framework.
- Frame orientation parameters. Digital photogrammetry, image capture,
- Aerophotogrammetric flight parameters and planning, arrangement of ground control points (GCP)
( reference books)
Lecture notes by the teacher
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6
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AGR/10
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48
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-
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Core compulsory activities
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ITA |
