Optimized UAV Trajectory Planning for Precision Agriculture Using Wireless Sensor Networks

The effectiveness of unmanned aerial vehicle (UAV) planning stands vital for precision agricultural services because it creates optimized resource utilization and raises productivity levels. The machine learning-based UAV flight planning system operates specifically for arable lands utilizing climate and wind power to generate secure and energy-efficient precise flight paths. KNN osmotic pressure (k-nearest Neighbors) enables the system to evaluate wind patterns and environmental parameters through its analysis process. A deep folding module (CNN) operates within the UAV system to detect pests after which it alerts operators about identified solutions. The current image classification system managed through openCV directs the independent operational capabilities of the CNN model which performs pest detection among plant species. The system enhances UAV-based farming automation by implementing intelligent mission programming and active pilot identification which results in cost reduction and enhanced harvest quality. The research demonstrates that using ML solutions develops efficient flight paths with accurate pest-detection systems which enable precise agricultural techniques for eco-friendly agriculture.