CNN Based Pest Detection and Recommender System for Plantain Trees

Plantain trees are susceptible to various pests, which can hinder their growth and productivity. This study proposes an innovative approach that merges Convolutional Neural Networks (CNN) with a recommender system to identify plantain tree pests and provide effective control recommendations. CNNs examine leaf photos for finding out the damage indicators and pests. Having been trained on a wide range of pest datasets, the machine learns from these photos to produce precise predictions. The system uses a vast knowledge base that includes professional recommendations, research, and historical data to provide users with customized pest management recommendations. By using this method, plantain cultivation is optimizer and crop yields are raised. The outcomes show how effective it is, which makes it a useful tool for farmers. Future plans call for adding real-time pest monitoring, extending the system's reach to include other plant diseases and pests, and leveraging user feedback to continuously improve it. Plantain agriculture benefits from this all-encompassing approach since it protects crops and increases their potential output.