Early Cotton Plant Disease Detection using Drone Monitoring and Deep Learning

India is the largest contributor of cotton production in the world but at recent times the cotton production has been decreased significantly due to various cotton plant diseases. The bacterial and fungal diseases affect the growth of the cotton at early stages and cause decrease in cotton production. Manual monitoring of these plant diseases is not possible as cotton plants are cultivated in huge acres of land. Early detection of cotton plant diseases prevents the rapid spread of disease to the whole cotton field. This project deals with the implementation of a deep learning model to detect the type of cotton disease that affects the cotton plant and to classify whether it is a fresh cotton leaf or diseased cotton leaf at earlier stages of plant growth. MobileNetV2, a CNN based model is implemented with the real time dataset. When MobileNetV2 is compared to other CNN models, it performs better in terms of model size, accuracy, and validation speed, demonstrating its superiority in the classification and identification of diseases affecting cotton plants. Using drones for real time field monitoring is an efficient technique. It also reduces time consumption. In this project STM32 Discovery board and the camera module is integrated with the drone and the developed deep learning model has been deployed to it for real time classification of cotton plant diseases. The farmer can take appropriate preventive measures when plant diseases are detected at the earlier stage. Accuracy, loss, precision and recall of the model were analyzed and considered as the evaluation metrics of the developed model. Final output such as type of cotton disease affected and the accuracy of disease prediction is displayed to the user. The location of infected plant is tracked using the GPS module integrated with the microcontroller board.