Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging.

Abstract

Background Gray 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. Results Based 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. Conclusions This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.

Plant phenotyping relevance

イチゴ葉の病害症状をハイパースペクトル画像から直接推定し、特徴抽出と分類器を開発・評価しているため、植物表現型取得法が中心である。

Code and data availability

The paper's hyperspectral strawberry leaf disease dataset and analysis are not publicly deposited; the authors state the data are available only from the corresponding author on reasonable request. No public code, model, or data URLs are provided.

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