研究目的
Investigating the use of hyperspectral and simulated Sentinel-2 data for the detection of mite infestation in its early stage on the grape leaves.
研究成果
The study concludes that hyperspectral sensing and Sentinel-2 data have significant potential for detecting low severity mites infestation in grape leaves. The LR based model performed best with an accuracy of 93.24% for hyperspectral data and 89.12% for simulated Sentinel-2 data. Field validation showed a mites detection accuracy of 83.33% using actual Sentinel-2 images, indicating good agreement with ground observations.
研究不足
The study acknowledges the challenge of variation in spectral reflectance of the leaves over time due to changes in chlorophyll content and developments in the cell structure, which makes the detection based on multi-temporal data difficult.
1：Experimental Design and Method Selection:
The study used hyperspectral remote sensing data collected from grape leaves with healthy and low infestations of mites using a spectroradiometer. Feature selection was performed using LASSO to identify optimum bands for classification.
2：Sample Selection and Data Sources:
Data was collected from 9 plots in Nashik district of Maharashtra, India, during the period of 15 Jan-18 Feb
3：List of Experimental Equipment and Materials:
20 A hand-held spectroradiometer (MS-720 from EKO Corporation, Japan) was used for data collection.
4：Experimental Procedures and Operational Workflow:
Spectral data was collected for grape leaves with low infestation of mites and healthy grape leaves. The data was normalized using spectral observations of barium sulfate powder.
5：Data Analysis Methods:
The selected bands were fed to classifiers such as RF, ANN, and LR to evaluate their performance. The potential of Sentinel-2 data was also evaluated by simulating the hyperspectral data to Sentinel-2 bands.
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