Computational method for cotton plant disease detection of crop management using deep learning and internet of things platforms

Abstract

Cotton is a major crop from income point of view in India. Cotton crops are damaged due to early fall off leaf or leaf will get infected due to diseases. Due to sudden change in climatic conditions, plant diseases occurred either scorching temperature in the crop filed or some pesticides will be required within a time. There are multiple systems to detect and restrain the diseases on a cotton leaf through soil monitoring in classification and identification of numerous diseases like bacterial blight, Alternaria, and many more. After disease detection, will be provided to the farmers using various machine learning algorithms and IoT-based system. In this paper, the main focus is on a new deep learning method, which investigates to automatically identify a diseased plant from leaf images of the cotton plant and IoT-based platform in collecting various sensor data for detecting climatic changes. The deep CNN model is developed to perform cotton plant disease detection using infected and healthy cotton leaf images by collecting images through the complete process used in training and validation for image preprocessing; augmentation and fine-tuning. Different test cases were accomplished to check the performance of the created model and make this new system economical and independent. This newly created system gives accuracy as efficient as possible for cotton plant disease detection and restrains by improving crop production, this paper provides an innovative path to researchers for developing a cotton plant disease identification system.