Beyond ReLU: Unlocking Superior Plant Disease Recognition with Swish

R. Bhuvanya, T. Kujani, R. Padmavathy, P. Matheswaran and P. Punitha
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R. Bhuvanya: Department of Computer Science and Engineering, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
T. Kujani: ��Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai, India
R. Padmavathy: ��Department of Electronics and Communication Engineering, Dr. NGP Institute of Technology, Coimbatore, India
P. Matheswaran: �Department of Computer Science and Engineering, K. Ramakrishnan College of Technology, Trichy, India
P. Punitha: �Department of Artificial Intelligence and Data Science, Tagore Institute of Engineering and Technology, Salem, India

International Journal of Innovation and Technology Management (IJITM), 2024, vol. 21, issue 08, 1-25

Abstract: Plant disease identification plays a crucial role in agricultural management, but the traditional methods are time-consuming and imprecise. This work contributes to the ongoing efforts to develop reliable and efficient solutions for automated plant disease diagnosis, ultimately aiding in the timely management and mitigation of agricultural challenges. This study investigated the effectiveness of various deep convolutional neural networks (CNNs) for automated plant disease identification from leaf images. By exploring deep and transfer learning techniques such as CNNs, InceptionNet, DenseNet 121, and ResNet-50, a Deep Convolutional Neural Network (DCNN) is proposed to categorize the leaf disease. Different activation functions, specifically Rectified Linear Unit (ReLU) and Swish, are employed to investigate their impact on model performance. The experiments revealed that the DCNN architecture, when paired with the Swish activation function, demonstrated 96% accuracy in plant disease identification.

Keywords: CNN; DCNN; leaf disease; ReLU; Swish; plant disease recognition (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219877024410013

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