Weed Detection in Sesame Seed

Contemporary agricultural systems encounter substantial obstacles in controlling weed proliferation, which diminishes harvest yields and escalates operational expenditures. This study introduces WeedScan AI, a novel deep learning architecture that utilizes satellite imaging and advanced computer vision methodologies for autonomous weed identification in farming environments. The framework employs a customized YOLOv8 neural network configuration to differentiate between cultivated plants (sesame) and unwanted vegetation with outstanding accuracy. Our approach integrates live satellite image analysis, sophisticated data enhancement methods, and precise mapping protocols to facilitate selective herbicide deployment. Validation experiments reveal superior performance achieving 99.2% identification accuracy, 0.3-second analysis duration, and potential to decrease chemical applications by 60% while preserving crop protection effectiveness. The web-enabled platform delivers farmers an accessible interface for field surveillance, providing economical solutions that have produced approximately $2.4 million in agricultural cost reductions. This investigation advances sustainable cultivation methods by reducing environmental consequences through precision farming technologies while improving crop productivity and financial feasibility for agricultural practitioners.