AI-Based Drone System for Pest and Stray Animals in Farms

Pests and stray animals are a major hazard to crops, particularly to small and marginal farmers, causing tremendous economic losses. Conventional methods of insect control are time consuming, costly and hard to amplify. This research proposes an AI manual drone system that combines real-time imaging and future analysis to an early detection of pests and stray animals. The system employs a low-cost unmanned aerial vehicle with a digital camera to photograph the area, avoid crop damage and cut down unnecessary pesticides. Images collected to identify pests and lost animals are treated using laptop vision strategies. A convolutional neural network (CNN) is trained upon a dataset including healthy crops, pest-infested plants and lost animals to verify the precision of high detection. The system controls the parameters by adjusting them based on environmental situations in order to dynamically minimize false positivity and make the detection more reliable. On top of this, real-time environmental data are used to predict potential insect invasions based on climatic factors so that prevention measures can be initiated. The system provides instant notice, which alerts farmers via mobile devices to allow timely action. Experimental results indicate 92.5% accuracy in detection, 7.5% false positive rate and average response time of 2.3 seconds, and provide quicker and accurate identification than conventional methods. In contrast to current single function solutions, this allows the doubleturning system automated agricultural monitoring and efficient insect control, and is a scalable and effective agricultural safety solution