Intelligent Drone Assisted Plant Disease Detectionand Precision Pesticide Spraying Using a Residual-Inception Deep Learning Framework

Deep learning techniques combined with unmanned aerial vehicles (UAVs) create new possibilities which will transform smartagricultural practices. This project introduces an innovative system which merges a Residual-Inception convolutional neuralnetwork with an intelligent drone spraying system to enable early detection and targeted treatment of plant diseases. Theresearch used a dataset which contained 38 crop disease classes and achieved a 94.5% validation accuracy after 10 trainingepochs. The proposed model combines multi-scale convolutional branches with residual shortcuts to improve feature learningwhile decreasing classification errors. The researchers created a mathematical waypoint generation algorithm which enablesUAVs to navigate through fields by creating efficient field coverage paths with optimal spray distribution. The researchersdeveloped a pesticide prioritization system which maps detected diseases to customized treatment plans based on diseaseseverity. The simulated results show precise classification results which successfully plan waypoints and create clear visualdisplays of the spraying operations. This Study demonstrates a scalable and economical method for executing precisionagriculture because it decreases chemical application while enhancing crop health management.