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.

Research Progress on Plant Responses to Stress Combinations in the Context of Climate Change

“…Integrated application and deeper mining of these data in combination with meta-analysis, CRISPR/Cas9 gene editing, and nanotechnology can improve our understanding of stress combinations [ 27 ]. Precision agriculture is the future direction of agricultural development, and the use of remote sensing data and machine learning, coupled with improved phenotyping and breeding methods, allows for the rapid discrimination of resistance phenotypes in plants through high-throughput methods [ 102 ], predicting plant pest and disease risks [ 103 , 104 , 105 ], controlling weeds [ 106 , 107 ], identifying environmental and nutrient status [ 108 ], and monitoring plant growth [ 109 ]. The combined use of these can accelerate the development of resistant plant varieties, favoring plant growth efficiency and tolerance to stress combinations [ 63 ].…”