Advancing Cauliflower Leaf Freshness and Disease Classification Using Deep Learning Models

Jisan, Sakib Alam, Das, Sourav Kumar, Naeen, Md. Julkar, Haldar, Nivedita, Chakraborty, Narayan Ranjan and Mojumdar, Mayen Uddin (2025) Advancing Cauliflower Leaf Freshness and Disease Classification Using Deep Learning Models. In: 19th INDIACom; 12th International Conference on Computing for Sustainable Global Development, INDIACom 2025, 02-04-2025-04-04-2025, Delhi.

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Abstract

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 1,598 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.

Item Type: Conference or Workshop Item (Paper)
Keywords: classification | Deep learning | leaf disease | ResNet50
Subjects: Physical, Life and Health Sciences > Agricultural science
JGU School/Centre: Jindal Global Business School
Depositing User: Mr. Luckey Pathan
Date Deposited: 01 Oct 2025 16:22
Last Modified: 01 Oct 2025 16:22
Official URL: https://doi.org/10.23919/INDIACom66777.2025.111156...
URI: https://pure.jgu.edu.in/id/eprint/10179

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