ArticlePlant methods2023
Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging.
Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Early Detection of Postharvest Potato Tuber Dry Rot Based on Hyperspectral Imaging and a Dual-Branch ResNet12-SE Spatial-Spectral Fusion Network.Journal of fungi (Basel, Switzerland) · 2026Article
- Melatonin and Selenium Enhance Strawberry Resistance toFoods (Basel, Switzerland) · 2026Article
- Research Progress on Plant Responses to Stress Combinations in the Context of Climate Change.Plants (Basel, Switzerland) · 2024Review
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge.
resultsBased on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases.
conclusionsThis study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.
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