AI-BASED SMART SPRAY TECHNOLOGY FOR PRECISION FUNGICIDE APPLICATIONS

The first goal of the project is to incorporate state-of-the-art (SOTA) deep-learning machine vision models into a high-performance computerized tractor sprayer controller for targeted pesticide applications on cucurbits.To achieve this we will train SOTA object detection and instance segmentation models to; 1) detect, identify and count cucurbit crop canopies, flowers and fruit, 2) detect common cucurbit diseases, and 3) utilize generative SOTA models to create abundant synthetic image data for model improvement. The second goal of the project is to design and build a pre-commercialization but full-scale grade prototype smart spray system. We will integrate sprayer controller with pulse width modulation system for valves,data acquisition, artificial lighting, dual boom system, self priming capabilities, estimation of spray usage, reporting and simulation functions into the spray system.