Cotton Leaves Disease Classification Using Custom Convolutional Neural Network (CNN) Model

Cotton is a major industrial crop for Bangladesh's economy. Bangladesh's textile sector generates 27% of the country's GDP. 95% of the cotton used in the textile sector is imported, which lowers the gross profit margin. The biggest barrier to reaching high production is a variety of leaf diseases. To guarantee good cotton production, early disease identification and the correct insecticide are essential. The classification of images using CNN models is particularly efficient and effective. In this study, we created a unique CNN model to identify and categorize five illnesses of cotton leaves. The model consists of five convolutional layer and four dense layers. The model has simple linear architecture. Both augmented and non-augmented data were used to evaluate this model. We used five different types of photos of cotton disease to train the model. For augmented and non-augmented data, our model produced accuracy levels of 98.5% and 94.64%, respectively.