Detection of Pest and Disease in Banana Leaf using Convolution Random Forest
In crop production, pest and disease detection is considered as one of the difficult tasks for the farmers. This paper aims to design a real time pest and disease detection system to recognize the pests in early stage by integrating Image processing techniques with Internet of Things (IoT) in banana plant. In this approach, the images are segmented using K-Mean clustering technique that identifies the pests. Subsequently, the category of pest is identified and is classified using convolution random forest. The various features of the pest and disease are used to train the convolution random forest to classify the pest pixel and disease pixels. Based on disease the organic pesticide is suggested using intelligent system of chatbot. The proposed methodology improvises accuracy and it assist the farmers in safeguarding the crop from damage by sending an alert message.