Cauliflower Disease Identification Using Deep Learning Techniques

Abhijeet Rachagoudar, Ashutosh Gebise, Lalitkumar Solapure, Prasanna Shirahatti, Veena Badiger

2025

Abstract

This paper presents a machine learning-based approach for the identification of diseases in cauliflower plants using deep learning techniques. The model, based on a CNN architecture, achieves high accuracy in classifying cauliflower diseases into four categories: Bacterial Spot Rot, Black Rot, Downy Mildew, and No Disease. The preprocessing is very comprehensive, including image resizing, normalization, and data augmentation, which enhances the model’s ability to generalize. F1-score, precision, and recall are some of the evaluation metrics to ensure a proper assessment of the model’s performance. The proposed solution will be helpful for farmers in early disease detection, thereby ensuring effective crop management and agricultural productivity. In addition, the study explores patterns and provides insights into potential enhancements through advanced architectures and dataset expansion. The results have proved that the model has an accuracy of 96.96%, thus it can be very useful in its practical world applications. Future work involves real-time monitoring systems and incorporation of domain-specific knowledge for robust disease diagnosis. Findings therefore stress the importance of automated solutions in the precision agriculture area, holding potential for large-scale deployment in agricultural sectors. This study would lay the ground for further studies on the usage of AI-based tools in sustainable agriculture.

Paper Citation

in Harvard Style

Rachagoudar A., Gebise A., Solapure L., Shirahatti P. and Badiger V. (2025). Cauliflower Disease Identification Using Deep Learning Techniques. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 827-833. DOI: 10.5220/0013603300004664

in Bibtex Style

@conference{incoft25,
author={Abhijeet Rachagoudar and Ashutosh Gebise and Lalitkumar Solapure and Prasanna Shirahatti and Veena Badiger},
title={Cauliflower Disease Identification Using Deep Learning Techniques},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={827-833},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013603300004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Cauliflower Disease Identification Using Deep Learning Techniques
SN - 978-989-758-763-4
AU - Rachagoudar A.
AU - Gebise A.
AU - Solapure L.
AU - Shirahatti P.
AU - Badiger V.
PY - 2025
SP - 827
EP - 833
DO - 10.5220/0013603300004664
PB - SciTePress