ECONOMIA E MECCANIZZAZIONE PER L'AGRICOLTURA DI PRECISIONE

Module MECHANIZATION OF AGRICULTURAL ACTIVITIES BY PRECISION FARMING TECHNIQUES

The course aims to provide disciplinary and professional skills for the application of techniques, machinery, and procedures in precision farming at both farm and territorial scales, in order to reduce environmental impact, working time, resource use, and production costs. It also aims to provide knowledge of the instruments and techniques required for field monitoring, including the use of Unmanned Aircraft Systems (UAS), and for translating field data into agronomic management practices using properly equipped machinery, models, and decision support systems. Furthermore, the course offers criteria for selecting machinery for the environmentally sustainable management of agro-ecosystems, in full respect of natural resources and the territory.

With reference to the Dublin Descriptors, at the end of the course the student will acquire:

Knowledge and understanding abilities
Knowledge of the principles underlying Precision Farming, of field‑mapping systems and sensors, of actuators and control systems that must be integrated into machines suitable for variable‑rate operations.

Applying knowledge and understanding abilities
Ability to recognise the functionalities of components, machines, and systems in order to understand their potential and possible applications.

Making judgements abilities
Ability to compare different solutions and select the one most appropriate for specific requirements.

Communication skills
Ability to correctly use technical terminology in order to interact and engage with specialists. Ability to simplify and make understandable, also to non‑specialists, the functionalities and distinctive characteristics of sensors, actuators, controllers, machines, and systems.

Learning skills
Ability to learn the topics covered through logical‑deductive methods rather than mnemonic approaches.

Lectures (21 hours) supported by slides, and exercises (42 hours) consisting of practical application analyses, seminars, and visits to local farms.

If the course is delivered in blended or remote mode, appropriate adjustments may be made to the above, in order to ensure consistency with the syllabus.

Knowledge of conventional agricultural machinery used for tillage, plant protection, fertilization, and harvesting.

Attending the classes is not mandatory, but strongly recommended, as it facilities the learning process and contributes to the achievement of the learning objectives.

The concept of precision agriculture will be explained by referring to its evolution. During the lectures, the following topics will be analyzed: farm information systems, remote sensing systems, and remote control platforms, including UAS, for crop monitoring; sensors for soil and crop surveys; positioning and guidance systems for agricultural machinery; yield mapping systems; and variable rate machines for soil tillage, sowing, fertilization, and crop protection. Additionally, precision farming applications for open-field herbaceous crops and arboreal crops will be examined.

The lecturer provides all the slides used to cover the topics outlined in the program during class, in order to serve as a guide for individual study. Therefore, lecture notes will also be important. Additionally, the following texts are recommended, while students are free to consult any other books related to the topics covered.

Oral examination covering the entire program.

The evaluation will take into account the relevance and quality of the answers provided, as well as the use of appropriate technical language, the ability to reason, and the capacity to connect the transversal knowledge acquired throughout the various parts of the program.

In detail, the following criteria will be applied:

Not acceptable
Knowledge and understanding of the topic: Substantial shortcomings and inaccuracies..
Ability to analyze and synthesize: Irrelevant with frequent generalizations Inability to summarise.
Use of references: Completely inappropriate.

18-20
Knowledge and understanding of the topic: Very modest with evident imperfections.
Ability to analyze and synthesize: Barely adequate.
Use of references: Just appropriate.

21-23
Knowledge and understanding of the topic: Knowledge is slightly above the minimum acceptable level.
Ability to analyze and synthesize: Able to perform correct analysis and synthesis. Arguments are presented in a logical and coherent manner.
Use of references: Use of standard references.

24-26
Knowledge and understanding of the topic: Good knowledge.
Ability to analyze and synthesize: Good analytical and synthesis abilities. Arguments expressed coherently.
Use of references: Use of standard references.

27-29
Knowledge and understanding of the topic: More than good knowledge.
Ability to analyze and synthesize: Considerable abilities of analysis and synthesis.
Use of references: He/She has examined the topics in depth.

30-30L
Knowledge and understanding of the topic: Excellent knowledge.
Ability to analyze and synthesize: Excellent abilities of analysis and synthesis.
Use of references: Significant in‑depth insights.

Learning assessment may also be carried out on-line, should the conditions require it.

To ensure equal opportunities and in compliance with current laws, students may request a personal interview in order to plan any compensatory and/or dispensatory measures based on educational objectives and specific needs. Students can also contact the CInAP (Centro per l’integrazione Attiva e Partecipata — Servizi per le Disabilità e/o DSA) through the referring professor within the department, (https://www.cinap.unict.it/content/referenti).

Exam dates can be found on the website of the Department of Agriculture, Food, and Environment at the following link: https://www.di3a.unict.it/courses/l-25/exams-calendar.

• Remote sensing systems.
• Positioning systems.
• Existing satellite networks.
• UAS (Unmanned Aircraft Systems): functions and limitations; usage issues.
• Proximity sensors for crop monitoring.
• Sensors for production mapping.
• Variable rate machines.