According to Food Agriculture Organization, by 2050 the world population will outnumber 9 billion people; it will be a must to increase food production by a 70%, compared to 2009 performances.

At the same time, farmers are striving to increase crops/farms production while:

• maximizing profits and minimizing costs without sacrificing product quality
• reducing waste of natural resources and use of chemicals, having a higher focus on sustainability to reduce environment impact

So, the motto here is Produce more with less.

Agriculture 4.0 arises in this context with the aim of better management of the different crops / farms, predicting the health of the plants and their needs, keeping track of the processes carried out and the data obtained in order to improve production. The Precision Farming technique is the key which gives farmers a boost to face and win this challenge.

The purpose of the thesis is to design and develop a new system capable of offer support on the decision-making process through the constant monitoring of the crops’ parameters, such as soil humidity, temperature, weather forecast, disease recognized, etc. This system exploits the patterns and paradigms of the Precision Farming technique, supported by the use of IoT device, a weather forecast provider, a satellite imaging provider and an AI detection model, so being able to provide the telemetry and the information needed and to recognized diseases and infestations’ patterns. In this way, by actively knowing the status of the plantation, it will be possible to proactively suggest the different actions to be undertaken, for example recommending when it's best to irrigate the field in order to optimize to exploitation of the resources (such as water, electricity to power up irrigation system, etc).

The thesis work started with the review of the topic through the analysis of previous studies, works, researches and strategies applied to operate in the different use cases, in order to evaluate the functionalities offered by the current solutions. The subsequent phase was the analysis of the sector current state, interviewing the sector stakeholders to be aware of the actually used techniques and their goals in order to discover their needs and daily challenges so to find out which are the better improvement opportunities.

Then, exploiting the information gathered in the previous steps, it began possible the define the strategies to apply to perform an efficient design and implementation of a prototype which will fulfil the stakeholders' needs, while improving the agricultural production process itself and making it more sustainable. The design and implementation of the prototype has been executed accordingly with the strategies built in the previous step using Microsoft's technology. It was been taken into account the challenges that the fields could have and the actual technological availability. The realization of the system had a focus on the IoT devices configuration, on the modelling of the AI model, on the API which connects them all and on the platform, where the data and the processes will be displayed.

[La tesi, il sommario e la presentazione non sono pubblicamente disponibili in quanto sotto embargo]