An Efficient Infectious Disease Detection in Plants Using Deep Learning

Abstract

Over the past decade, agriculture has suffered reduced productivity from climate change and improper water, fertilizer, and pesticide use, fueling plant diseases. Pathogens pose the main threat, impacting crop yield and quality. Early detection and targeted treatments are crucial to improve both yield and quality. To address this, we have carried out deep learning-based approaches and published ours works in conferences and journSal. Those works are briefly discussed in the paper as follows: (i) Empirical work on different plant datasets is conducted to analyze the hyperparameters of the neural network. (ii) The research minimizes misclassifications by leveraging an ensemble-based strategy with AlexNet, ResNet, and VGGNet across seven plant leaf image datasets. The complexity of plant disease diagnosis in diverse conditions is tackled through a hybrid deep learning strategy, exemplified in the cardamom plant disease detection approach. (iii) An innovative deep learning-based approach is introduced to precise plant disease detection, crucial in the face of similar symptoms and imbalanced data. The proposed Multilevel Feature Fusion Network (MFFN) incorporates adaptive attention mechanisms, enhancing robustness by considering diverse network features. (iv) With cardamom plant disease classification utilizing U2-Net for background removal and EfficientNetV2 for classification, the network excels the performance on images with complex background, with this generated benchmark dataset with a complex background. This research work produced good results by achieving 99% accuracy on the tomato plant and 98.28% accuracy on the cardamom leaf dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.