This survey paper provides a comprehensive overview of common plant leaf diseases, evaluates traditional and deep learning techniques for disease detection, and summarizes available datasets. It also explores Explainable AI (XAI) to enhance the interpretability of deep learning models' decisions for end-users.
The paper first discusses various common leaf diseases, including scab, black spot, powdery mildew, blight, mosaic, Marssonina blotch, frogeye spot, and rust. It then reviews the available datasets for plant leaf disease detection, such as the Cassava Leaf Disease Dataset, Tomato Leaf Disease Dataset, Corn and Maize Leaf Disease Dataset, Plant Village Dataset, Plant Pathology 2020 Dataset, and Plant Pathology 2021 Dataset.
The literature review covers conventional methods, deep learning techniques, and recent advancements in vision transformers for plant leaf disease detection. Conventional methods rely on feature extraction and traditional classification algorithms, while deep learning approaches leverage convolutional neural networks and transformer-based architectures to achieve impressive performance.
To address the lack of interpretability in deep learning models, the paper introduces Explainable AI (XAI) and its role in plant leaf disease detection. XAI techniques, such as SHAP, LIME, Grad-CAM, and Microsoft's InterpretML, are discussed to enhance the transparency and trustworthiness of the deep learning-based solutions.
The survey highlights the potential future research directions, including disease stage identification, detection of multiple disease infections, and quantification of disease severity. By consolidating this knowledge, the paper offers valuable insights to researchers, practitioners, and stakeholders in the agricultural sector, fostering the development of efficient and transparent solutions for combating plant diseases and promoting sustainable agricultural practices.
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by Saurav Sagar... at arxiv.org 04-29-2024
https://arxiv.org/pdf/2404.16833.pdfDeeper Inquiries