Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging
By X. Chu, Wei Wang, Xinzhi Ni, Chunyang Li, Yufeng Li. 2020
55 citations recorded by the source corpus.
Abstract Maize is easily to be infected with fungi in field, and cause a considerable yield reduction and mycotoxin contamination. The objective was to assess, with the use of a near-infrared hyperspectral imaging (900–1700 nm), the difference between healthy and fungi infected maize kernels of three hybrids (i.e., ‘JingKe968’, ‘JingNuo2000’ and ‘XianYu335’, representing dent, waxy, and semi-flint endosperms, respectively). Two sampling strategies, i.e., pixel-wise (PW) and object-wise (OW), were examined using principal component analysis (PCA), successive projections algorithm (SPA) and support vector machine (SVM) modelling. In object-wise analysis, average spectra of whole individual kernel were analyzed. OW-PCA-SVM models developed using PC1 and PC3 through PC6 achieved accuracies of 99.00%, 97.96% and 97.87% for the three maize hybrids respectively. Eight (1168, 1344, 1414, 1428, 1520, 1269, 1691 and 1205 nm), eight (1161, 1336, 1450, 1638, 1266, 1698, 1189 and 1520 nm), and ten (1168, 1375, 1673, 1602, 1340, 1189, 1245, 1534, 1417 and 1262 nm) optimal wavelengths were respectively selected by SPA, and similar results (100%, 98.98% and 98.94%) were achieved. In pixel-wise analysis, classification accuracies at kernel level for the three varieties were 92.00%, 95.3% and 99.32% for PW-PCA-SVM models, and 100%, 100% and 100% for PW-SPA-SVM models, respectively. The results indicated that both object-wise and pixel-wise methods can be used for classification of fungi infected maize kernels. Pixel-wise classifications was superior in generating visualization maps for presenting the spatial information of infected kernels.
Subfields: Analytical Chemistry, Biomedical Engineering, Biophysics
View Paper: https://www.semanticscholar.org/paper/f20e57ec499aaf0ddb2839d952a5cc25b44f35ad
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Citations in the corpus
- Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review (2021, background): “In another study, Chu et al. (2020) involved the use of object wise and pixel-wise approach for feature extraction from hyperspectral images for classi fi cation of infected maize kernels.”