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Institute of Facility Agriculture, Guangdong Academy of Agricultural Science

Xin Liang

Huazhong Lu

Sai Xu

Changyuan Zhang

Jian Peng

Changxiang Fan

Guangjun Qiu

The present disclosure discloses a non-destructive detection method and system forBradley, which includes: placing a lychee sample to be detected on a conveyor belt; acquiring sample information of the lychee sample based on visible/near-infrared light and X-ray imaging technology; wherein the visible/near-infrared light acquires internal quality features of the lychee sample; the X-ray imaging technology acquires internal density variation features of the lychee sample; analyzing the sample information according to a preset data processing and analysis method based on PLSR and a support vector machine to obtain a classification result of the lychee sample. The present disclosure uses the visible/near-infrared transmission spectrum and the X-ray imaging feature fusion in combination with PLSR to improve the detection accuracy of the lychee sample of the testing set to 92.92%, and in combination with the support vector regression to obtain a classification accuracy of 94.44% of the testing set.

Non-destructive detection method and system forBradley

An intelligent recognition method of hyperspectral image of parasites in raw fish relates to optical detection technology, and includes step 1: obtaining a hyperspectral image of the raw fish in a wavelength range from 300 to 1100 nm; step 2: extracting a grayscale image of the hyperspectral image at a wavelength value of 437 nm, and obtaining a position range of fish meat in the grayscale image by performing a median filtering process and a binarization process on the grayscale image; step 3: extracting spectral signals of pixel points in the position range of the hyperspectral image, performing a first-order derivative process on the spectral signals, and import the spectral signals after the first-order derivative process into a preset first model, a second model, and a third model for analysis. The method can accurately distinguish the parasite body in the raw fish.

Intelligent recognition method of hyperspectral image of parasites in raw fish

An automatic peeling and splitting device for citrus fruits includes a bracket, a pomelo fixing module, and a pomelo peeling module arranged on the bracket. The pomelo fixing module includes a cylinder arranged on an upper part of the bracket, a fixed block arranged on a power output end of the cylinder, and a motor arranged on the fixed block, the power output end of the motor is connected with a rotation shaft, an end of the rotation shaft is sleeved with an air sac, and the end of the rotation shaft is further hinged with a plurality of arc baffles, a concave member of the arc baffle is bonded to a surface of the air sac, an outer convex edge of the arc baffle faces outside of the air sac; the rotation shaft and the power output end of the cylinder are both extended vertically downward.

Automatic peeling and splitting device for citrus fruits

A feature extraction method of fruit spectrum includes taking a vector of each wavelength point in spectrum of samples as source data, and acquiring a sorting of all vectors by processing the source data by SPA; according to the sorting of the vectors, acquiring distribution points of each sample on a coordinate system; acquiring classification results of the samples by destructive analysis, and acquiring a number of first sample categories; acquiring a first Euclidean distance between the first sample categories; according to a sorting of the wavelength points, acquiring distribution points of each sample on the coordinate system; acquiring a number of second sample categories; acquiring a second Euclidean distance between the second sample categories; determining whether the first Euclidean distance is less than the second Euclidean distance; determine a (M+2)-th vector to be valid or invalid based on a comparison result.

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Guangdong, China

Xin Liang