Modern Challenges and Opportunities of Machine Learning: How AI Supports Agriculture

Agriculture is the key sector for food production, as well as one of the main components of GDP.

For several years now, this centrality has been accompanied by great transformations: globalization and the counter-response of “glocalism”, climate change, social factors such as the growing attention to organic products and the sustainability of the entire production chain. Added to these evolutions are the problems in the strict sense: ISTAT data from 2023 highlight the difficulties farmers face in coping with the decline in production, mainly caused by climate changes and rising energy costs.

In this complex scenario, digital technologies show their potential. In fact, in 2022 the market for agriculture 4.0 in Italy grew by 31% compared to the previous year. Artificial Intelligence, thanks to its advanced capabilities in data analysis, automation, and optimization, allows agricultural productivity to be improved.

Applications of AI in Agriculture

• Crop management: data related to temperature, soil moisture, and other weather conditions are collected in real-time thanks to IoT sensors. AI algorithms then process these variables to optimize planting schedules, soil watering regularity, and identify the optimal ripening time for fruit harvesting. The help of technology thus improves crop yields, reducing resource waste.

• Plant disease recognition: for farmers, it is very important to promptly recognize diseases affecting crops: acting early is key to stopping the spread of pathogens and minimizing damage. Through Computer Vision, it is possible to identify, from drone or camera photos, signs of plant disease and easily understand the cause of the stress. At Aidia, we have developed a similar solution for one of our clients, with the aim of preventing and reducing vine diseases.

• Soil monitoring: AI can analyze soil samples, examining their chemical composition and intrinsic characteristics. Depending on the nutrients present in the soil, farmers can more accurately choose the plots of land that best suit a particular crop. This information is also important in the management of fertilization processes.

• Automation: the need to respect the rhythms of nature while simultaneously managing the speed of wholesale processes leads agricultural entrepreneurs to perform many operations in reduced times. Agricultural robots, integrated with AI, can perform harvesting, pruning, and planting operations efficiently and accurately. This way, all timelines are met, and human labor can be redistributed to less strenuous, supervisory tasks.

• Smart irrigation systems: these systems constantly monitor soil moisture and keep an eye on weather forecasts. By analyzing and merging these two factors, they can automatically regulate irrigation, minimizing waste and ensuring that plants receive the optimal amount of water.

• Demand and supply prediction: these systems constantly monitor soil moisture and keep an eye on weather forecasts. By analyzing and merging these two factors, they can minimize waste and ensure that plants receive the optimal amount of water.

Artificial Intelligence thus has the potential to successfully support agricultural entrepreneurs, offering cutting-edge solutions to face daily challenges and enable sustainable production.