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TY - JOUR AU - Shahid, Muhammad Taimoor AU - Zohaib, Muhammad PY - 2025 DA - 2025/11/08 TI - Cucumber Leaf Diseases Recognition Based on Deep Convolutional Neural Networks JO - ICCK Transactions on Intelligent Systematics T2 - ICCK Transactions on Intelligent Systematics JF - ICCK Transactions on Intelligent Systematics VL - 2 IS - 4 SP - 238 EP - 247 DO - 10.62762/TIS.2025.363963 UR - https://www.icck.org/article/abs/TIS.2025.363963 KW - cucumber diseases KW - deep learning KW - convolutional neural network (CNN) KW - transfer learning KW - VGG16 KW - InceptionV3 KW - image classification KW - plant disease recognition AB - Cucumber cultivation is a vital component of Pakistan's agricultural economy and is a key vegetable in the national diet. However, crop yield and quality are severely threatened by diseases like powdery mildew and downy mildew. Early and accurate disease detection is critical for implementing targeted treatment and preventing widespread infection. This study proposes a deep learning-based framework for the automated recognition of cucumber leaf diseases. We designed and trained a custom Convolutional Neural Network (CNN) from scratch and compared its performance against powerful pre-trained transfer learning models, including VGG16 and InceptionV3. The models were evaluated on a dataset of cucumber leaf images. Our experimental results demonstrate that the transfer learning approach significantly outperforms the custom CNN. Specifically, the VGG16 model achieved the highest accuracy of 98.76% in classifying the diseases. The findings confirm that advanced deep learning models can serve as effective tools for rapid and precise plant disease diagnosis, offering a valuable application for sustainable agricultural practices. SN - 3068-5079 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Shahid2025Cucumber,
author = {Muhammad Taimoor Shahid and Muhammad Zohaib},
title = {Cucumber Leaf Diseases Recognition Based on Deep Convolutional Neural Networks},
journal = {ICCK Transactions on Intelligent Systematics},
year = {2025},
volume = {2},
number = {4},
pages = {238-247},
doi = {10.62762/TIS.2025.363963},
url = {https://www.icck.org/article/abs/TIS.2025.363963},
abstract = {Cucumber cultivation is a vital component of Pakistan's agricultural economy and is a key vegetable in the national diet. However, crop yield and quality are severely threatened by diseases like powdery mildew and downy mildew. Early and accurate disease detection is critical for implementing targeted treatment and preventing widespread infection. This study proposes a deep learning-based framework for the automated recognition of cucumber leaf diseases. We designed and trained a custom Convolutional Neural Network (CNN) from scratch and compared its performance against powerful pre-trained transfer learning models, including VGG16 and InceptionV3. The models were evaluated on a dataset of cucumber leaf images. Our experimental results demonstrate that the transfer learning approach significantly outperforms the custom CNN. Specifically, the VGG16 model achieved the highest accuracy of 98.76\% in classifying the diseases. The findings confirm that advanced deep learning models can serve as effective tools for rapid and precise plant disease diagnosis, offering a valuable application for sustainable agricultural practices.},
keywords = {cucumber diseases, deep learning, convolutional neural network (CNN), transfer learning, VGG16, InceptionV3, image classification, plant disease recognition},
issn = {3068-5079},
publisher = {Institute of Central Computation and Knowledge}
}
ICCK Transactions on Intelligent Systematics
ISSN: 3068-5079 (Online) | ISSN: 3069-003X (Print)
Email: [email protected]
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