Application of Successive Projection Algorithm in Nondestructive Detection of Soluble Solid Content in Apple

Yan, Xingwei; Bi, Shuhui; Shen, Tao

doi:10.1109/icamechs.2019.8861659

Cited by 1 publication

(1 citation statement)

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“…In this research, a ratio of 1 in initial chromosome sequences was set to 0.4, the crossover rate was set to 0.5, the mutation rate was set to 0.1, and the number of iterations was set to 200. Quite different from GA, SPA generally begins with identifying an initial feature band randomly, then the projections from the rest bands to the initial feature band are calculated [36]. The band with the largest projection value is selected as the second feature band.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning

Wei

Liu

et al. 2023

Processes

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The classification of GM and non-GM maize kernels is fundamental for further analysis of the gene action in maize. Therefore, a complete and novel detection scheme based on near-infrared spectra was designed to distinguish GM and non-GM maize kernels. Hyperspectral images (935–1720 nm) of 777 maize kernels from 3 kinds were captured, and the average spectra of the maize kernels were extracted for modeling analysis. The classical modeling methods based on feature engineering were first studied, and the backpropagation neural network–genetic algorithm model showed the best performance with a prediction accuracy of 0.861. Then, novel modeling methods based on deep learning were developed. To dig out the interactive information between different bands and match the application scenarios, the original spectra were transformed into two-dimensional matrices before establishing the deep learning models. A modified convolution neural network (i.e., VGG net) with dilated convolution was finally constructed to classify the maize kernels, and the prediction accuracy reached 0.961. This research provides a referential and novel way to detect GM maize kernels. Future research will improve the detection scheme for monitoring unauthorized GM organisms by introducing the visualization technology of deep learning.

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Section: Feature Extraction and Selectionmentioning

confidence: 99%