In this study, a 400-1000nm hyperspectral camera can be used, and the products of Hangzhou Color Spectrum Technology Co., LTD
In this study, a 400-1000nm hyperspectral camera can be used, and the products of Hangzhou Color Spectrum Technology Co., LTD
FS13 conducts related research. The spectral range is 400-1000nm, and the wavelength resolution is better than 2.5nm, up to 1200
Two spectral channels. Acquisition speed up to 128FPS in the full spectrum, up to 3300Hz after band selection (multi-zone support
Domain band selection). FS13 conducts related research. The spectral range is 400-1000nm, and the wavelength resolution is better than 2.5nm, up to 1200
Two spectral channels. Acquisition speed up to 128FPS in the full spectrum, up to 3300Hz after band selection (multi-zone support
Domain band selection).
The silkworm (Bombyx mori Linnaeus) is an economic insect that eats mulberry and spins silk, so it is also called the silkworm. Silkworms originated in ancient China and were gradually domesticated by the original silkworms inhabiting mulberry trees. As early as 5,000 years ago, the ancients had mastered the technology of planting mulberry and raising silkworms. In ancient times, sericulture made great contributions to the development of economy and culture. At present, mulberry silkworm industry promotes the development of rural economy, improves the living standard of farmers, and is one of the important sideline industries in agricultural production. In addition, the silkworm industry is in a leading position in the international market and plays an important role in world trade, creating a large number of foreign exchange reserves for our country. Therefore, the sustainable development of mulberry silkworm industry has extremely important economic value and significance.
The traditional chemical detection technology needs to pretreat the tested samples, the operation process is complicated, and a lot of chemical reagents are consumed. The accuracy of enzymatic rapid detection technology is low, so it can only be used for primary screening. Spectral nondestructive testing technology is not representative because of one-sided information. Therefore, a fast, reliable and comprehensive nondestructive testing of mulberry leaves is sought.
The method of pesticide residue is of great significance in crop safety detection. Hyperspectral imaging technology is a new non-destructive testing technology combining imaging technology and spectrum technology, which has the advantages of no need to destroy the measured object, comprehensive information acquisition and high detection accuracy. In this paper, hyperspectral imaging technology combined with spectral processing and analysis methods were used to study the pesticide residues in mulberry leaves, not only to study whether there are pesticide residues in mulberry leaves and the identification of pesticide residues, but also to study the quantitative detection of chlorpyrifos pesticide residues in mulberry leaves. The research content of this paper provides technical support for sericulture industry and strong guarantee for sericulture farmers' income, and promotes the sustainable and in-depth development of sericulture industry, which has extremely important theoretical value and practical significance.
In this paper, hyperspectral imaging technology combined with spectral processing and analysis methods was used to quantitatively detect the content of chlorpyrifos in mulberry leaves. Mulberry leaves with different chlorpyrifos residues were used as test objects to obtain hyperspectral images of mulberry leaves in the range of 390-1050nm by hyperspectral imager. ENVI software is used to determine the region of interest of the blade and calculate the average spectral data of the region. The correlation coefficients between the mean spectral data of mulberry leaf samples and the corresponding chemical values determined by gas chromatograph were calculated, and 5 waves were selected according to the waveform diagram of correlation coefficient and wavelength.
The wavelengths corresponding to peaks and troughs are used as characteristic wavelengths (561.25, 680.86, 706.58, 714.32, 724.66nm). Based on spectral data at characteristic wavelength, a quantitative detection model of mulberry leaf residues was established by using multiple linear regression and support vector regression. The correction set determination coefficient R² of the MLR prediction model is 0.730, the root mean square error RMSEC is 38.599, and the prediction set determination coefficient R is obtained. Is 0.637, and the root mean square error RMSEP is 47.146. The correction set determination coefficient R3 is 0.920, the root-mean-square error RMSEC is 21.073, the prediction set determination coefficient R3 is 0.874, and the root-mean-square error RMSEP is 27.719. Through comparative analysis: SVR prediction model has better performance than MLR prediction model, so vision-near-infrared hyperspectral imaging technology combined with SVR prediction model can be used to nondestructive detection of chlorpyrifos residues in mulberry leaves.