IMPROVING THE PERFORMANCACE OF SMART FARM PEST MONITORING AND CONTROL SYSTEM USING MACHING LEARNING TECHNIQUE

This paper introduces a smart farm pest monitoring and detection system utilizing machine learning techniques. The objective of this study is to develop an intelligent system for monitoring and detecting pests in rice farms using machine learning. The methodology involves data collection, data processing, feature extraction, and training of K-nearest neighbor (K-NN) algorithm with the feature vectors to generate the smart pest monitoring and detection model. The model was implemented and tested through simulation approach. Comparative analysis was used for the validation model. The results demonstrate a notable 2% improvement when compared to existing classification models.