ArticlePlant phenomics (Washington, D.C.)2023
Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- LViM: Language-Infused Visual Mamba for apple leaf pests and diseases precise segmentation in complex environments.Plant phenomics (Washington, D.C.) · 2026Article
- Thermal image segmentation in weedy fields via synthetic RGB-trained models and GAN-based cross-modality alignment.Plant phenomics (Washington, D.C.) · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- PlantIF: Multimodal semantic interactive fusion via graph learning for plant disease diagnosis.Plant phenomics (Washington, D.C.) · 2026Article
- Grading evaluation of haploid fertility restoration traits based on inception-ResNet in maize.Plant phenomics (Washington, D.C.) · 2025Article
- SSP-MambaNet: An automated system for detection and counting of missing seedlings in glass greenhouse-grown virus-free strawberry.Plant phenomics (Washington, D.C.) · 2025Article
- A Lightweight Framework for Protected Vegetable Disease Detection in Complex Scenes.Food science & nutrition · 2025Article
- AutoGP: An intelligent breeding platform for enhancing maize genomic selection.Plant communications · 2025Article
- Intelligent deep learning architecture for precision vegetable disease detection advancing agricultural new quality productive forces.Frontiers in plant science · 2025Article
- Detection techniques for tomato diseases under non-stationary climatic conditions.Frontiers in plant science · 2025Article
- High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images.Plants (Basel, Switzerland) · 2024Article
- Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis.Plant phenomics (Washington, D.C.) · 2024Article
- Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection.Frontiers in plant science · 2024Review
- Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.Frontiers in plant science · 2024Review
- Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.Plant phenomics (Washington, D.C.) · 2024Article
- Sugarcane nitrogen nutrition estimation with digital images and machine learning methods.Scientific reports · 2023Article
- Eff-3DPSeg: 3D Organ-Level Plant Shoot Segmentation Using Annotation-Efficient Deep Learning.Plant phenomics (Washington, D.C.) · 2023Article
- Erratum to "Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning".Plant phenomics (Washington, D.C.) · 2023Article
- Image-based classification of wheat spikes by glume pubescence using convolutional neural networks.Frontiers in plant science · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning and computer vision have become emerging tools for diseased plant phenotyping. Most previous studies focused on image-level disease classification. In this paper, pixel-level phenotypic feature (the distribution of spot) was analyzed by deep learning. Primarily, a diseased leaf dataset was collected and the corresponding pixel-level annotation was contributed. A dataset of apple leaves samples was used for training and optimization. Another set of grape and strawberry leaf samples was used as an extra testing dataset. Then, supervised convolutional neural networks were adopted for semantic segmentation. Moreover, the possibility of weakly supervised models for disease spot segmentation was also explored. Grad-CAM combined with ResNet-50 (ResNet-CAM), and that combined with a few-shot pretrained U-Net classifier for weakly supervised leaf spot segmentation (WSLSS), was designed. They were trained using image-level annotations (healthy versus diseased) to reduce the cost of annotation work. Results showed that the supervised DeepLab achieved the best performance (IoU = 0.829) on the apple leaf dataset. The weakly supervised WSLSS achieved an IoU of 0.434. When processing the extra testing dataset, WSLSS realized the best IoU of 0.511, which was even higher than fully supervised DeepLab (IoU = 0.458). Although there was a certain gap in IoU between the supervised models and weakly supervised ones, WSLSS showed stronger generalization ability than supervised models when processing the disease types not involved in the training procedure. Furthermore, the contributed dataset in this paper could help researchers get a quick start on designing their new segmentation methods in future studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.