The use of artificial intelligence in agriculture: state of art and future prospects

Agriculture is experiencing a phase of profound change, driven by the need to respond to global challenges such as theIncrease in food demand, The scarcity of natural resourcesand the growingclimatic variability. In this context, theArtificial intelligence (ia)He is establishing himself as one of the most promising levers to make the agricultural sector moreefficient, sustainable and resilient.

Through the advanced data analysis, automatic learning capacity and interaction with intelligent sensors and machinery, the IA allows you tooptimize production processes, predict critical events and support strategic decisionsin real-time. From the targeted management of crops to the automation of activities in the fields, applications are increasingly widespread and sophisticated.

The goal is not only to increase the yields, but to do it in a waysmarter, reducing the environmental impact and enhancing the available resources. The adoption of the AI ​​in agriculture does not simply represent a technological evolution, but onecultural and organizational transformationintended to redefine the future of the sector.

Obstacles and priorities for full digitization

The path to onefull digitizationis still dotted withStructural and cultural obstacleswhich slow down the adoption of technologies by SMEs. Among the most evident critical issues, thescarcity of digital skills, both within companies and in the territories less served by training policies and technological hubs. The lack of qualified personnel represents a tangible brake, especially for micro and small businesses that do not have resources dedicated to innovation.

Alongside the theme of skills, theresistance to change. In many entrepreneurial realities, a traditional business vision remains, which struggles to understand the strategic value of digitization. This attitude leads to underestimate the return on investment and to postpone choices that would instead beessential for growth and survivalon the market.

From an infrastructure point of view, inequalities that penalize the internal or less urbanized areas, where theaccess to fast connectivity and Cloud servicesIt is not always guaranteed. Fill this gap is a priority, just as thestrengthening of synergiesBetween public and private, universities, research centers and businesses.

To overcome these blocks and stimulate an effective transformation, it is essential to invest informationcontinues, oriented to both young people and entrepreneurs and workers already active. In parallel, it is necessarystrengthen public incentivesand simplify access to tools such as theTransition Plan 4.0, which offers tax credit for investments in capital goods, software and training.

Digitization can no longer be considered an option, but onenecessary condition to innovate, grow and compete. Tackling the barriers that limit its diffusion with decision is the first step to build a more modern, resilient and connected production system.

Current technologies and applications

L 'Artificial intelligenceIt is already the protagonist in different areas of agriculture, offering tools capable of improving the efficiency and precision of agricultural activities. The technologies currently in use are based onAutomatic learning algorithms, artificial vision systems, neural networks and predictive analysis platforms.

Among the most common applications:

• Cultures monitoring through satellite images and drones, with automatic analysis of the vegetative state, of the level of hydration and the onset of pathologies.
• Early diagnosis of diseases and infestationsThanks to the visual recognition and databases trained on thousands of images.
• Provision of the yieldsThrough models that integrate historical data, weather, land quality and cultivation practices.
• Irrigation optimizationBy means of intelligent sensors that detect the humidity of the soil in real-time and guide automatic irrigation systems.
• Autonomous agricultural robotsUsed by sowing, collection, deserves and localized treatments, reducing the need for labor and the use of chemicals.

L 'precision agriculture(on which you can find detailed information onIdegreen.it) represents one of the most mature contexts for the integration of the AI, in which the analysis of big data is translated intoconcrete actions on the field, with tangible benefits for productivity and sustainability.

Despite the progress, the spread of these solutions is still stilldysomogeneous, influenced by economic, regulatory and infrastructure factors. However, success cases are increasingly numerous, a sign of an ongoing change that promises to extend on a large scale.

Advantages and impacts on production efficiency

The use ofArtificial intelligenceIn the agricultural field, it brings with it a series of measurable advantages, both at the level of a single company and along the entire agri -food supply chain. One of the most immediate effects concerns theIncrease in productivity, grazie alla possibilità di pianificare e gestire le operazioni agricole in modo più razionale e tempestivo. L’IA consente di raccogliere e interpretare enormi quantità di dati in tempo reale, permettendo decisioni rapide e basate su informazioni oggettive.

Un altro beneficio rilevante è il miglioramento della qualità dei raccolti. Grazie al monitoraggio continuo delle colture e all’identificazione precoce di eventuali stress o patologie, è possibile intervenire in modo mirato e limitare i danni, mantenendo standard qualitativi elevati. L’agricoltura guidata dall’IA è anche più precisa nell’uso delle risorse, in particolare acqua, fertilizzanti e fitofarmaci, riducendo sprechi e costi operativi.

Sul piano ambientale, l’adozione dell’IA contribuisce a un’agricoltura più sustainable. L’impiego ottimizzato degli input agricoli comporta una riduzione dell’impatto sul suolo e sulle risorse idriche, limitando la dispersione di sostanze nocive e preservando la fertilità dei terreni. Inoltre, l’automazione intelligente permette di contenere le emissioni di CO₂ legate ai processi produttivi.

A livello strategico, l’IA può rafforzare la capacità di adattamento delle aziende agricole di fronte a scenari climatici o economici sempre più incerti. Analisi predittive, simulazioni e modelli dinamici supportano scelte più consapevoli, rendendo il sistema produttivo complessivamente più resiliente ed efficiente.

Current limits and adoption barriers

Nonostante il potenziale trasformativo dell’Artificial intelligence, his adoption in the agricultural sector still meets differentstructural critical issues. One of the main barriers is represented byinitial costslinked to the implementation of technologies, which include the purchase of hardware and software, the installation of systems and the training of staff. These investments can be prohibitive above all for small and medium -sized agricultural enterprises, which make up most of the production fabric in many areas of the world.

Another significant obstacle is thepoor digital literacyin the rural field. In many areas, the lack of IT and technical skills makes it difficult to adopt the AI ​​-based solutions, despite being potentially advantageous. To this are added theinfrastructure disparity, such as the lack of stable internet connections or adequate devices for monitoring and data transmission. The question ofagricultural dataIt constitutes a further critical node. Often the information collected is incomplete, uneven or not compatible with each other. The quality and accessibility of data are fundamental for the correct operation of the IA algorithms, and their deficiency can compromise the effectiveness of the entire system. In addition, thefragmentation of digital platformsIt hinders full interoperability between devices, software and actors of the supply chain.

The appearance linked to thetrust in intelligent technologies. The complexity of IA models, often perceived as opaque or difficult to control, can generate diffidence in agricultural operators. The transparency of the algorithms, theSensitive data protectionAnd respect for privacy are crucial elements to encourage conscious and sustainable adoption.

Overcome these barriers requires an integrated approach, which involvespublic policies, targeted training and partnerships between technological and agricultural sector, with the aim of making the ia accessible and useful for everyone.