高光谱成像的黄瓜病虫害识别和特征波长提取方法

doi:10.3964/j.issn.1000-0593(2024)02-0301-09

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Abstract Cucumber downy mildew and libria sativa are serious diseases and insect pests that restrict the development of the cucumber industry. In order to realize the rapid identification of cucumber diseases and insect pests, hyperspectral imaging technology and machine learning were used to explore the characteristic wavelengths of cucumber diseases and insect pests, which laid a foundation for the development of practical cucumber diseases and insect pests identification equipment based on multispectral imaging. This study used a hyperspectral imaging system to collect hyperspectral images of asymptomatic leaves, downy mildew leaves and leaf miner-infected leaves. According to the size of the leaf spot area, several regions of interest (ROI) were selected in the spot area, and the average reflectance data of each ROI was calculated as the original spectral data of the leaf. The samples were divided into training sets and test sets in a 3∶1 ratio by the Kennard-Stone algorithm. Direct orthogonal signal correction (DOSC), multiplicative scatter correction (MSC) and moving average(MA) were used to preprocess the original spectral data. Variable iterative space shrinkage approach (VISSA), competitive adaptive reweight sampling method (CARS), iteratively retains informative variables (IRIV) and shuffled frog leaping algorithm (SFLA) were used to extract characteristic wavelengths, respectively obtains 53, 20, 26, 10 characteristic wavelengths. Then, the successive projections algorithm (SPA) was used to perform secondary dimensionality reduction on the characteristic wavelength data, and finally, the characteristic wavelengths extracted by VISSA-SPA were 455, 536, 615, and 726 nm. The characteristic wavelengths extracted by CARS-SPA were 452, 501, 548 and 578 nm. The characteristic wavelengths extracted by IRIV-SPA were 452, 513, 543 and 553 nm. The characteristic wavelengths extracted by SFLA-SPA are 462, 484, 500 and 550 nm. Support vector machine (SVM), Elman neural network and random forest (RF) modeling were carried out for the full-band and characteristic wavelength data. The results showed that the full band spectral data preprocessed by MA had the best recognition effect, in which the OA of the MA-RF model reached 97.89% and the Kappa coefficient was 0.97. The MA-VISSA-RF model had the best effect among the models built by the data of the characteristic wavelength first extracted, with 98.19% OA and 0.97 Kappa coefficient. MA-IRIV-SPA-SVM model had the best effect among the models built by quadratic dimensionality reduction data, with OA 96.23% and Kappa coefficient 0.95. The results showed that hyperspectral imaging technology had a good effect on the identification of cucumber downy mildew and the insect pest, and 452, 513, 543, 553 nm could be used as the characteristic wavelength for identification of cucumber downy mildew and the insect pest, providing a theoretical basis for developing cucumber disease and insect pest identification equipment, providing a theoretical basis for the development of cucumber disease and insect pest rapid identification equipment.

Key words：Hyperspectral imaging; Machine learning; Characteristic wavelengths; Downy mildew; Leafminer-infected

Received: 2022-06-14 Accepted: 2022-11-10

ZTFLH:

O433

Corresponding Authors: ZHAI Chang-yuan E-mail: zhaicy@nercita.org.cn

Cite this article:

LI Yang,LI Cui-ling,WANG Xiu, et al. Identification of Cucumber Disease and Insect Pest Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 301-309.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)02-0301-09 OR https://www.gpxygpfx.com/EN/Y2024/V44/I02/301

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