23 October 2018 Onion irrigation treatment inference using a low-cost hyperspectral scanner
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Abstract
Many studies have shown that hyperspectral measurements can help monitor crop health status, such as water stress, nutrition stress, pest stress, etc. However, applications of hyperspectral cameras or scanners are still very limited in precision agriculture. The resolution of satellite hyperspectral images is too low to provide the information in the desired scale. The resolution of either field spectrometer or aerial hyperspectral cameras is fairly high, but their cost is too high to be afforded by growers. In this study, we are interested in if the flow-cost hyperspectral scanner SCIO can serve as a crop monitoring tool to provide crop health information for decision support. In an onion test site, there were three irrigation levels and four types of soil amendment, randomly assigned to 36 plots with three replicates for each treatment combination. Each month, three onion plant samples were collected from the test site and fresh weight, dry weight, root length, shoot length etc. were measured for each plant. Meanwhile, three spectral measurements were made for each leaf of the sample plant using both a field spectrometer and a hyperspectral scanner. We applied dimension reduction methods to extract low-dimension features. Based on the data set of these features and their labels, several classifiers were built to infer the field treatment of onions. Tests on validation dataset (25 percent of the total measurements) showed that this low-cost hyperspectral scanner is a promising tool for crop water stress monitoring, though its performance is worse than the field spectrometer Apogee. The traditional field spectrometer yields the best accuracy as high as above 80%, whereas the best accuracy of SCIO is around 50%.
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Tiebiao Zhao, Alexander Koumis, Haoyu Niu, Dong Wang, and YangQuan Chen "Onion irrigation treatment inference using a low-cost hyperspectral scanner", Proc. SPIE 10780, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, 107800D (23 October 2018); doi: 10.1117/12.2325500; https://doi.org/10.1117/12.2325500
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