Beyond ReLU: Unlocking Superior Plant Disease Recognition with Swish
R. Bhuvanya, T. Kujani, R. Padmavathy, P. Matheswaran, P. Punitha
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.