Smart Agriculture Revolution: Edge AI for Precision Farming on Low-Power Embedded Systems

Abstract

The farming industry is facing unprecedented challenges in order to feed a quickly growing global population while meeting environmental sustainability issues and resource limitations. Conventional farming techniques marked by uniform input distribution over entire fields show great inefficiencies and contribute heavily to environmental degradation in the form of nutrient runoff and excessive chemical application. Edge AI technologies combined with precision agriculture platforms offer revolutionary solutions in the form of advanced sensor networks, machine learning techniques, and automated control systems that optimize input use with spatial and temporal accuracy. Solar-powered embedded platforms based on ARM processors and FPGA AI accelerators allow autonomous agricultural monitoring in different environmental conditions while consuming minimal power. Multi-sensor fusion structures integrate high-resolution imaging, multispectral sensors, soil sensors, and meteorological weather stations to form robust real-time monitoring networks. State-of-the-art convolutional neural networks utilizing large agricultural datasets provide early detection of diseases, accurate irrigation control, and accurate yield prediction abilities. Large-scale deployments exhibit remarkable improvements in resource utilization, including considerable water reduction, pesticide use, and fertilizer needs, while sustaining or increasing crop yields. Environmental impact reports record significant decreases in farming inputs, endorsing the conservation of aquifers and preserving biodiversity. Integration possibilities with new technologies such as drone monitoring, robotic harvesting, and blockchain traceability systems hold the promise of complete digital farming systems across entire agricultural value chains.