摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-技术的新研究-成像技术是一篇报道的主题。摘要:根据NewsRx编辑对泰安市的新闻报道,研究表明:“生育期的确定和色坐标的预测是比较果实品质的关键。提出了一种基于高光谱成像技术的番茄生育期判断和色坐标预测模型。”我们的新闻记者引用山东农业大学的一篇研究，“利用最有效的颜色坐标预测模式L来获得彩色视觉图像，首先对不同生长期（绿熟、变色、半熟和半熟）的高光谱图像进行匹配，并对颜色坐标（L*，A*，B*，C，”结果表明，LDA模型预测效果最好，预测离子集准确率为93.1%，预测效果最好，预测精度为93.1%。利用竞争自适应重加权采样(CARS)和连续投影算法(SPA)选择有效波长，利用偏最小二乘回归(PLSR)、多元线性回归(MLR)、主成分回归(PCR)和支持向量机回归(SVR)建立色度预测模型，最后利用最优模型计算番茄各像素的颜色，生成颜色坐标的视觉分布图。
Abstract
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."
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