Plant Disease Detection Using Convolutional Neural Network - Kalamkar, Yash; Nenwani, Harsha; Lahane, Nikita; Wagh, Poonam; Tayde, S.R.; Walchale, P.M (International Journal of Computer Sciences and Engineering, 2025)
In this work, a diverse dataset is utilized alongside robust preprocessing techniques to develop an optimized convolutional neural network (CNN) model for plant disease detection. The dataset contains images of different crops affected by various diseases, forming the training base. Effective preprocessing steps are undertaken to improve data quality and boost model accuracy.The CNN is thoughtfully engineered, incorporating convolutional and pooling layers to capture critical patterns from the input images. Following extensive training, the model attains a remarkable accuracy of 92.23% in identifying diseases, demonstrating the power of CNNs to revolutionize plant disease detection and provide a valuable tool for farmers and agricultural experts.By leveraging machine learning in agriculture, this approach can greatly enhance the early detection of crop diseases, minimizing losses and boosting productivity. These technological strides ultimately support global food security and promote sustainable farming, paving the way for a brighter future in agriculture.