PRECISION AGRICULTURE ENHANCED WITH PHYSICS-INFORMED NEURAL NETWORKS (PINNS)

Abstract

Precision agriculture boosts harvests and safeguards the soil by managing water, nutrients, and earth with exacting care—like drip lines feeding each plant a slow, perfect trickle. In this study, we introduce one unified method that blends Physics-Informed Neural Networks (PINNs) with an IoT-driven precision farming system, where fingertip-sized soil sensors feed fresh data straight into the model. The proposed method combines physical models of soil moisture, nutrient movement, and crop growth with deep learning, delivering sharper accuracy and staying reliable even when conditions shift—like after a sudden summer storm leaves the fields shimmering with rain. In real corn, vegetable, and wheat fields, tests found water use dropped as much as 35%, fertilizer needs fell by a quarter, and pesticide use was nearly halved—yet yields jumped 15–20%, with corn ears standing tall and golden in the sun. The PINN-powered system hit over 94% accuracy in predicting yields, edging past traditional data-driven models like a sprinter leaning into the tape at the finish. The findings suggest that physics‑informed AI could push precision farming toward being more sustainable and resilient, such as tweaking irrigation at dawn to match the cool, damp feel of the soil.