首页|Shandong Agricultural University Reports Findings in Imaging Technology (Growth period determination and color coordinates visual analysis of tomato using hyper spectral imaging technology)
Shandong Agricultural University Reports Findings in Imaging Technology (Growth period determination and color coordinates visual analysis of tomato using hyper spectral imaging technology)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Technology - Imaging T echnology is the subject of a report. According to news reporting out of Tai'an, People's Republic of China, by NewsRx editors, research stated, "Growth period determination and color coordinates prediction are essential for comparing posth arvest fruit quality. This paper proposes a tomato growth period judgment and co lor coordinates prediction model based on hyperspectral imaging technology." Our news journalists obtained a quote from the research from Shandong Agricultur al University, "It utilizes the most effective color coordinates prediction mode l to obtain a color visual image. Firstly, hyperspectral images were taken of to matoes at different growth periods (green-ripe, color-changing, half-ripe, and f ull-ripe), and color coordinates (L*, a*, b*, c, h) were obtained using a colori meter. The sample set was divided by the sample set partitioning based on joint X-Y distances (SPXY). The support vector machine (SVM), K-nearest neighbors (KNN ), and linear discriminant analysis (LDA) were used to discriminate growth perio d. Results show that the LDA model has the best prediction effect with a predict ion set accuracy of 93.1%. In addition, effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and successive pr ojections algorithm (SPA), and chromaticity prediction models were established u sing partial least squares regression (PLSR), multiple linear regression (MLR), principal component regression (PCR) and support vector machine regression (SVR) Finally, the color of each pixel of the tomato is calculated using the optimal model, generating a visual distribution image of the color coordinate."
Tai'anPeople's Republic of ChinaAsiaDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineImaging TechnologyMachine LearningSupport Vector MachinesTechnologyVector Machi nes
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