Applying Convolutional Neural Networks for Disease Detection in Crop Images

Ensuring agricultural production and food security relies on the early and precise identification of crop diseases. It may be tedious, error-prone, and time-consuming to use traditional approaches to identify diseases. In this study, we investigate how Convolutional Neural Networks (CNNs) may be used to improve and automate the process of identifying diseases in crop photos. Our approach use deep learning to swiftly and reliably identify and classify a broad variety of agricultural diseases. We trained our CNN model on a diverse dataset that included images of crop leaves in various states of health. The dataset covers a broad variety of common illnesses and numerous kinds of crops, which guarantess that our model is resilient and can be applied to other situations. To avoid overfitting and boost the model’s performance, we used sophisticated data augmentation methods. The CNN model surpasses traditional machine learning methods in terms of accuracy, as shown by a significant reduction in false positives and false negatives. The model’s capacity to process and analyse images in real-time makes it beneficial in the field of disease management. This research emphasises the possibility of incorporating convolutional neural networks (CNNs) into precision agricultural methods, giving farmers a formidable instrument for early disease detection and prompt response to reduce crop losses. Improving the model’s scalability for largescale agricultural applications, adding more disease kinds to the dataset, and increasing the dataset are all areas that will be the focus of future effort.