AI-Powered Precision Agriculture: A Technical Review

Abstract

The agricultural industry is undergoing a transformative revolution through the integration of artificial intelligence and precision farming technologies. This technical review discusses a novel application of using computer vision and machine learning systems for precision weed management - specifically, sprayer technologies designed to revolutionize row crop agriculture. Modern precision agriculture technology utilizes complex AI-based systems using camera arrays in combination with convolutional neural networks to identify weeds in real-time and operate a targeted herbicide application accordingly. These implementations have very high classification accuracy across many species of crops and weeds while also functioning at speed across the field, even with unchanging environmental influences. The technology, used on the selective treatment principle, can recognize crops (desired) and weed species (unwanted) through complex pattern recognition and multispectral image processing.  The newest implementations have additional edge computing capacity, with sophisticated sensor fusion and precision control for indicated spray application rates, and can respond very rapidly while remaining accurate at the targeted spatial scale of treatment. Based on economic considerations, it is likely to be more expected if new economies are kept at the level of farm operator rather than regional implications impacting the wider agricultural economy due to reduced costs, stable crop yields, and reduced resource costs. Environmental benefits are likely to be most considerable when all environmental dimensions are considered, which may include reducing chemical exposure to non-target beneficial organisms, and reduced overall chemical load on the environment, e.g., via risk to groundwater contamination. Future options will allow improved machine learning, expanded identification databases, and consolidation with broad field management platforms embracing autonomous farming systems.