AI-Based Smart Spray Technology For Precision Fungicide Applications

This proposal will be submitted to the program area 'Plant Production and Protection (Engineering) (Topic Area 8.13) and we will address the priorities: b. Plant Protection against Abiotic and/or Biotic Stresses (i. Improved Chemical Application Technology and ii. Monitoring Detection and Management). Current cucurbit production practices involve multiple broadcast applications of fungicides from planting to harvest. Our goal is to devise an AI-based system that administers fungicides only where the crop canopy or disease is present. The specific objectives are: (1) Create advanced AI models for detection and identification of cucurbit crop canopies flowers fruits and disease symptoms; (2) Develop a full-scale prototype smart spray system using the AI models and machine vision for precise pesticide applications on cucurbits. We prioritize cucurbits as our initial focus due to an existing image database disease susceptibility and their vining growth pattern which often results in off-target applications. This offers a substantial chance to minimize pesticide use especially early in the season. Our approach facilitates rapid training for the detection of various cucurbit types in diverse production environments. We can produce ample synthetic image datasets for model training even with limited original images with the use of generative AI. This is crucial for early detection of exotic diseases which lack large image datasets thus preventing potential industry-wide damage if such diseases are introduced. Ultimately we will commercialize a tractor-mounted or autonomous system that applies fungicides selectively and monitors disease.