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Dealing with Unbalanced Data in Leaf Disease Detection: A Comparative Study of hierarchical classification, clustering-based undersampling and reweighting-based Approaches

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Procedia Computer Science 2023年 225卷 4891-4900页

作者： Haythem Ghazouani Walid Barhoumi Ezzeddine Chakroun Abdellah Chehri Université de Tunis El Manar Institut Supérieur d'Informatique d'El Manar Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA) LR16ES06 Laboratoire de recherche en Informatique Mode´lisation et Traitement de l'Information et de la Connaissance (LIMTIC) 2 Rue Abou Rayhane Bayrouni 2080 Ariana Tunisia Université de Carthage Ecole Nationale d'Ingénieurs de Carthage 45 Rue des Entrepreneurs 2035 Tunis-Carthage Tunisia Department of Mathematics and Computer Science Royal Military College of Canada Girouard Building Room 320

Precision agriculture plays a crucial role in optimizing crop yield, reducing environmental impact, and ensuring sustainable agricultural practices. Early detection and accurate diagnosis of leaf diseases are essential for preventing significant losses in crop production and maintaining food security. However, the inherent challenge of class imbalance in leaf disease datasets poses a significant obstacle for machine learning algorithms. In this paper, we explore and compare different techniques for handling class imbalance in leaf disease detection to improve the accuracy and reliability of machine learning models in the context of precision agriculture. We investigated the performance of different methods for leaf disease detection using the challenging New Plant Diseases Dataset (NPDD), which consists of image-based plant leaves. Our experiments reveal promising results, particularly with the hierarchical approach, achieving an accuracy of 97.17%. The outcomes of our study contribute to the growing body of knowledge in precision agriculture by providing a comprehensive analysis of techniques for handling class imbalance in leaf disease detection. Furthermore, our findings serve as a valuable resource for researchers and practitioners in the field, offering guidance on selecting and implementing the most effective approaches to tackle class imbalance challenges and improving the overall performance and reliability of machine learning models in the domain of precision agriculture.

关键词： Leaf disease detection unbalanced data clustering-based undersampling, reweighting-based training, hierarchical classification

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