With global population growth and intensified climate change, traditional agriculture is facing unprecedented challenges. The rapid evolution of AI technology is bringing revolutionary changes to the agricultural field, and precision agriculture is moving from a concept to the field.
The smart planting system collects dozens of environmental parameters such as soil moisture, temperature, light intensity, nutrient content, etc. in real time through a sensor network deployed in farmland. These data are fed into a deep learning model for analysis, which can accurately determine the current growth stage requirements of crops and automatically adjust irrigation, fertilization, shading, and other operations. Compared with traditional timed and quantitative irrigation, AI driven integrated water and fertilizer systems can save 30% to 50% of water and increase crop yields by 15% to 25%.
Intelligent irrigation is one of the most mature applications in precision agriculture. The unmanned aerial vehicle (UAV) patrol system based on computer vision and remote sensing technology can identify the growth status and pest and disease situation of crops from the air. Drones equipped with multispectral cameras can monitor NDVI (Normalized Difference Vegetation Index), timely detect areas of water shortage or uneven nutrition, and guide irrigation equipment to carry out targeted replenishment. This' on-demand irrigation 'model not only saves valuable water resources, but also avoids soil salinization caused by excessive irrigation.
AI image recognition technology has shown amazing potential in crop pest control. Farmers only need to take photos of their leaves with their mobile phones, and AI models can identify the type and severity of diseases within seconds, and provide accurate medication recommendations. The "Shennong" large model developed by the Chinese Academy of Sciences has covered the identification of more than 1000 crop diseases, with an accuracy rate of more than 95%.
The core value of AI empowering precision agriculture lies in transforming experience driven into data-driven. The AI system continuously optimizes the decision model through continuous learning, extracts patterns from single planting data, and makes the planting plan for the next season more accurate. This ability of continuous iteration has led agricultural production from "relying on the weather to eat" to "knowing the weather and working". With the popularization of edge computing and 5G technology in the agricultural scene, every crop in the future is likely to receive personalized precision care, which will be a qualitative leap in agricultural productivity.