Files and links (1)
Published (Version of Record)CC BY-NC V4.0, Open Access
Journal article
Automatic pixel-level annotation for plant disease severity estimation
Computers and electronics in agriculture, Vol.241, 111316
2026
Abstract
Plant disease adversely impacts food production and quality. Alongside detecting the disease, estimating its severity is important in managing the disease. Artificial intelligence deep learning-based techniques for plant disease detection are emerging. Unlike most of these techniques, which focus on disease recognition, this study addresses various plant disease-related tasks, including annotation, severity classification, lesion detection, and leaf segmentation. We propose a novel approach that learns the disease symptoms, which are then used to segment disease lesions for severity estimation. To demonstrate the work, a dataset of barley images was used. We captured the images of barley plants inoculated with diseases on test-bed paddocks at various growth stages. The dataset was automatically annotated at a pixel level using a trained vision transformer to obtain the ground truth labels. The annotated dataset was applied to train salient object detection (SOD) methods. Two top-performing lightweight SOD models were used to segment the disease lesion areas. To evaluate the performance of the SODs, we have tested them on our dataset and several other datasets, including the Coffee dataset, which has expert pixel-level labels that were unseen during the training step. Several morphological and spectral disease symptoms, including those akin to the widely used ABCD rule for human skin-cancer detection, i.e., asymmetry (A), border irregularity (B), colour variance (C), and diameter (D), are learned. To the best of our knowledge, this is the first study to incorporate these ABCD features in plant disease detection. We further extract visual and texture features using the grey level co-occurrence matrix (GLCM) and fuse them with the ABCD features. For the coffee dataset, our method achieved 82+% detection accuracy on the severity classification task. The results demonstrate the performance of the proposed method in detecting plant diseases and estimating their severity.
Details
- Title
- Automatic pixel-level annotation for plant disease severity estimation
- Authors/Creators
- Masoud Rezaei - Murdoch UniversityDean Diepeveen - Murdoch UniversityHamid Laga - Murdoch UniversityFerdous Sohel - Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, Australia
- Publication Details
- Computers and electronics in agriculture, Vol.241, 111316
- Publisher
- ELSEVIER SCI LTD
- Number of pages
- 17
- Identifiers
- 991005839953907891
- Copyright
- © 2025 The Authors.
- Murdoch Affiliation
- Centre for Biosecurity and One Health; School of Information Technology; Centre for Healthy Ageing
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Metrics
84 File views/ downloads
4 Record Views