Cotton Disease Detection Using TensorFlow Machine Learning Technique

Agriculture is a main source of income for farmers in India. Farmers produce many seasonal local crops based on their location. Cotton is the most produced crop across India. Cotton is a commercial crop, and farmers get good capital from cotton. This will increase the income of the farmer. However, one of the basic problems with cotton is that it is easily exposed to many diseases. These diseases need to be identified as early as possible to avoid production loss. In this paper, the CNN algorithm is used to create the prediction model by leveraging the TensorFlow's Keras API. This model is further used in mobile app development which helps the farmers identify cotton disease and recommend the pesticides which can be used to overcome the disease. The TensorFlow open-source platform was used to prepare the ML model. The TensorFlow Tflite model is created, and after that, the model is converted into the Core ML model, which is used in iOS app to make the disease predication. Google's core API is used to convert the TensorFlow model into the Core ML model. The label dataset was used to create the model. The Swift language is used in app development. The model accuracy was around 90%. Currently, boll rot and fungal leafspot disease are detected in this app. However, the app can be further extended for other cotton diseases too. © 2022 Sandeep Kumar et al.