Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.