Developments in deep learning approaches for apple leaf Alternaria disease identification: A review

Abstract

Apple tree leaf diseases (ATLDs) can be accurately identified and addressed early to prevent the diseases from spreading, minimize the need for chemical pesticides and fertilizers, increase apple quality and production, and preserve the healthy growth of apple varieties. To overcome such challenges, different Deep Learning (DL) approaches have been developed to early detect apple leaf diseases. In this paper, the data from 2010 to 2024 has been taken for analysis, and it has been observed that many of the researchers have utilized different types of datasets for disease detection. Moreover, Deep Learning (DL) and Machine Learning (ML) have been mostly utilized for the detection and identification of apple leaf Alternaria diseases. It has also been observed from the previous work that Support Vector Machines (SVM), Random Forests (RF), XGBoost, and many more are the most common approaches utilized by the researchers. On the other hand, DenseNet, MobileNet, Convolutional Neural Network (CNN), and Vision Transformer are the deep learning approaches utilized by the researchers. Furthermore, we have also given a brief analysis of each approach along with a comparative analysis such as lightweight CNNs and Attention-based mechanisms, Transfer Learning (TL), Localization techniques, Vision Transformer (ViT), and Severity estimation techniques. Emphasizing their methods, datasets, performance metrics, and real-world applications. This study explores the proposed models’ approaches, feature selection and extraction techniques, data capturing conditions, accuracy, types of datasets used in the experiments, and their resources. Our research findings indicate that although DL approaches have significant potential for improving disease management in agriculture. There is a crucial need for a more scalable, robust, and flexible solution to handle numerous agricultural conditions and disease complexities. By methodically and comprehensively analyzing the collected data, this study aims to facilitate valuable resources for researchers aiming to design, develop, and implement DL-based systems for apple leaf disease detection and identification, ultimately contributing to sustainable agriculture and improved food security.