Abstract
Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. This research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874–1734 nm coupled with chemometrics. Reflectance was extracted from regions of interest (ROIs) in the hyperspectral image of each sample. Firstly, principal component analysis (PCA) was applied to conduct a cluster analysis with the first several principal components (PCs). Then, two methods including X-loadings of PCA and random frog (RF) algorithm were used and compared for optimizing wavebands selection. Least squares-support vector machine (LS-SVM) methodology was employed to establish discriminative models based on the optimal and full wavebands. Finally, area under the receiver operating characteristics curve (AUC) was utilized to evaluate classification performance of these LS-SVM models. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929. These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals.
Highlights
Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants
The infection of rapeseed petals is the first step for the development of S. sclerotiorum on rapeseed plants because mycelium on the infected petals can penetrate into tissues of other organs[5]
Some broadband peaks or valleys occurred in the near infrared (NIR) region in Fig. 1, it can be explained by NIR spectral region contained rich information relevant to the hydrogen containing bonds[30]
Summary
Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929 These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals. The disadvantages of these methods include expensive, labor-intensive, time-consuming, and tedious extraction procedures, what’s more, these detection methods mostly pointed at an individual plant, and are limited for the large-scale agricultural production[11] It would be beneficial if a rapid, reliable and nondestructive technique is implemented in detecting the infected petals, so as to predict the risk of disease and provide a guideline for spraying. Rumpf et al.[29] applied support vector machines (SVM) and spectral vegetation indices to realize early detection and classification of various plant diseases based on hyperspectral reflectance. Mahlein et al.[18] mapped spectral reflectance enabled the detection and detailed description of diseased tissue on the leaf level based on hyperspectral imaging
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
