Agro-artificial intelligence has come to solve many problems in agriculture

In 10 years, when generative AI becomes far more advanced and fully autonomous, it will manage all aspects of agriculture—from seed to table.

This is the view of Monica Houston, AI and machine learning manager at Tria Technologies, a subsidiary of Avnet, who provides evidence in her article published on the Global Ag Tech Initiative portal.

Agriculture is a significant part of the global economy, accounting for 60% of GDP in countries like India, for example. Farmers have always struggled to manage their fields and yields, which are affected by various conditions, some of which are now worsening. These include a growing number of severe weather events due to climate change—droughts, floods, soil erosion, biodiversity loss, and more, including the impact of human activity on the planet. Then there are ongoing issues like labor shortages, pests, and diseases affecting crops and livestock, evolving regulations and standards, and the constantly changing expectations of food producers, retailers, and consumers. Additionally, there is increasing concern about environmental preservation and pollinator protection.

The adoption of technology in agriculture has become a critical way to address some of these challenges, boost productivity, and ensure farmer safety. Precision agriculture is one such system, helping farmers manage crops, maximize yields, reduce effort, and optimize processes. The Association of Equipment Manufacturers (AEM) reported in 2021 that the use of precision agriculture technologies increased farmer productivity by 4%, while reducing fertilizer use by 7%, herbicide use by 9%, and water use by 4%.

Data collection and the Internet of Things (IoT), combined with GPS, geographic information systems, remote sensing, and satellite imagery, have enabled farmers to improve their methods, but even more can be achieved with AI and robotics.

The analytical firm StartUs Insights predicts that the value of AI in agriculture will reach $4.7 billion USD by 2028, growing at an annual rate of over 23%.

Machines used in fields today are already smaller and smarter, equipped with sensors and local AI processing, or edge AI. Sensor data can be analyzed and used for real-time decision-making to better manage crops.

Sensor data can also be used to monitor the condition of agricultural machinery through predictive maintenance techniques, which notify maintenance personnel before machines fail. This is enabled by advanced processing boards, such as those from Tria Technologies, equipped with multiple processors, sensors, and AI capabilities. Machine learning on the Tria RASynBoard, for example, with its Syntiant NDP120 neural decision processor and Renesas RA6M4 main microcontroller, can be used in predictive maintenance operations to identify faulty equipment parts before issues arise. Onboard sound and vibration sensors determine whether a machine needs a new part or just maintenance.

Modern automated equipment relies on data sent to the cloud for processing, which is unstable and unreliable for remote areas like fields and farms.

However, the general trend is to keep data processing local, on the machine itself, offering several benefits, including higher productivity, better soil analysis, crop monitoring, pest detection, and irrigation management. For remote areas and vehicles, battery-powered solutions are highly suitable, and thanks to the latest generation of processors from companies like NXP, Qualcomm, and Renesas, such boards deliver very high performance with edge AI capabilities at very low power consumption.

The labor shortage issue can be addressed with automated machines, robots, and drones capable of making decisions on the go, using machine learning techniques underlying their analysis.

Combating climate change can be achieved with precision equipment equipped with AI that analyzes weather conditions, soil, and plant health at any time, deciding the best times to sow crops, water them (or stop watering), selectively treat pests and diseases (i.e., where most needed), harvest, and more. Precision agriculture using AI will save energy, water, pesticides, and herbicides.

Computer vision can also be applied in projects where robots are used for watering plants, and drones are used to select field areas for pesticide spraying. Automating such processes will also reduce human exposure to harmful chemicals.

These robots operate either autonomously, navigating fields using sensors, or are manually controlled via apps. Agricultural machinery, such as tractors and harvesters, is increasingly becoming automated, with some using SLAM (simultaneous localization and mapping) technology to successfully navigate their environment and overcome obstacles.

In the near future, agricultural AI will play an even greater role in agriculture, with tractors and agricultural robots responding directly to verbal or textual communication in human language. In the longer term, in 5–10 years, when generative AI becomes far more advanced and fully autonomous, it will manage all aspects of agriculture—from seed to table. Analytical AI and generative AI will transform how crops are grown, harvested, and distributed, while ensuring optimized and efficient farming methods with minimal human involvement. AI will also provide suitable ways to ensure sustainability even under intense economic pressures, thereby shaping the future of this industry.

Tags: agricultural sector, ИИ