Advancing Cauliflower Leaf Freshness and Disease Classification Using Deep Learning Models

This study introduces a novel method for cauliflower leaf disease detection using deep learning from raw data. Cauliflower is an economically significant crop that faces substantial challenges due to highly pathogenic leaf diseases, which severely impact yield and quality. The inability to implement effective management practices exacerbates these issues. Accurate disease detection is significant for effective disease management and sustainable agriculture. This study uses a deep learning approach to classify cauliflower leaf diseases, exploreing the application of three convolutional neural networks. A dataset comprising <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1, 5 9 8}$</tex> images was utilized for training and evaluation. Various image pre-processing techniques were applied to enhance model performance. Experimental results indicate that ResNet50 outperformed VGG16 and VGG19, achieving perfect classification accuracy. These findings underscore the precise detection of cauliflower leaf diseases, and the future development of automated agricultural systems.