Mahmoud M. Hussein

Work place: Faculty of Computers and Information Menoufia University / Computer Science / Shebin Elkom, 32511, Egypt



Research Interests: Data Mining, Machine Learning, Software Engineering


Mahmoud M. Hussein: received his BSc. and MSc. in Computer Science from Menoufia University, Faculty of Computers and Information in 2006 and 2009 respectively and received his PhD in Software Engineering from Swinburne University of Technology, Faculty of Information and Communications Technology in 2013. His research interest includes Software Engineering, Data Mining, Machine Learning, Data Privacy, and Security

Author Articles
Plant Disease Detection Using Deep Learning

By Bahaa S. Hamed Mahmoud M. Hussein Afaf M. Mousa

DOI:, Pub. Date: 8 Dec. 2023

Agricultural development is a critical strategy for promoting prosperity and addressing the challenge of feeding nearly 10 billion people by 2050. Plant diseases can significantly impact food production, reducing both quantity and diversity. Therefore, early detection of plant diseases through automatic detection methods based on deep learning can improve food production quality and reduce economic losses. While previous models have been implemented for a single type of plant to ensure high accuracy, they require high-quality images for proper classification and are not effective with low-resolution images. To address these limitations, this paper proposes the use of pre-trained model based on convolutional neural networks (CNN) for plant disease detection. The focus is on fine-tuning the hyperparameters of popular pre-trained model such as EfficientNetV2S, to achieve higher accuracy in detecting plant diseases in lower resolution images, crowded and misleading backgrounds, shadows on leaves, different textures, and changes in brightness. The study utilized the Plant Diseases Dataset, which includes infected and uninfected crop leaves comprising 38 classes. In pursuit of improving the adaptability and robustness of our neural networks, we intentionally exposed them to a deliberately noisy training dataset. This strategic move followed the modification of the Plant Diseases Dataset, tailored to better suit the demands of our training process. Our objective was to enhance the network's ability to generalize effectively and perform robustly in real-world scenarios. This approach represents a critical step in our study's overarching goal of advancing plant disease detection, especially in challenging conditions, and underscores the importance of dataset optimization in deep learning applications.

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