Plant Disease Detection using Image Processing and IoT

Agriculture is the backbone of Indian economy. Almost 70% of farmers still involved directly or indirectly in traditional ways of doing agriculture. In the current scenario agriculture-based plant diseases are of the significant concern as they reduce the productivity and quality of the harvest. Apart from pathogens, climatic conditions also induce various plant related diseases. The observation of plant leaf diseases requires close observation. Awareness of human eye is not so much tougher so as to observe minute variations in the infected part of leaf. Image processing techniques are employed to enhance the quality of images and its texture features. Banana is regarded to be one of the most beneficial plants. Banana leaf is highly prone to diseases like Black Sigatoka, Yellow Sigatoka, Bunchy top virus. This approach will enhance productivity of crops. This project decides about the nature of the leaves. It utilizes the sensor gadgets to recognize the parameters like temperature, moistness and shade of the leaves. Hence, it is mandatory to monitor the environmental parameters of the agriculture field which is done using RFC (Random Forest Classifier) technique from the GLCM (Gray Level Co-occurrence Matrix) features. The images of the crops will be taken by sensor cameras and these images will be sent to the cloud server via raspberry pi3 model. Plant disease detection required steps are image acquisition, image–preprocessing, image segmentation, feature extraction and classification. This project aims to confirm that RFC-GLCM based classification performs better for hill banana dataset.