Authors

Abstract

Using hyperspectral imaging technology for rapid, non-destructive detection of soybean grain moisture content provides technical support for high-quality soybean harvesting. A total of 90 samples of soybean grains from different varieties were collected, with hyperspectral images acquired in the wavelength range of 900–1700 nm. The moisture content of each soybean grain sample was determined using the direct drying method as specified in GB 5009.3-2016. The samples were divided into a calibration set and a prediction set based on a 4:1 ratio using the sample partitioning method of Joint X-Y Distance. Eight preprocessing methods were applied to the raw spectral data, including baseline correction, moving average, Savitzky-Golay filtering, normalization, standard normal variate transformation, multiple scatter correction, first derivative, and deconvolution. Feature wavelengths were then extracted using the successive projections algorithm and the competitive adaptive reweighted sampling algorithm. Finally, a partial least squares regression model for predicting the moisture content of soybean grains was developed based on these feature wavelengths. The results show that the correlation coefficient and the root mean square error of the optimal model for the prediction set were 0.92 and 0.2371, respectively. The moisture spectrum inversion model can precisely and rapidly predict the moisture content of soybean grains non-destructively, thereby determining the timing of mechanical soybean harvesting and enhancing the quality of soybean harvesting, storage, and processing.

Abstract in Chinese

采用高光谱成像技术实现大豆籽粒含水率的快速无损检测，为大豆高质量收获提供技术支撑。采集了90个不同品种大豆籽粒样本在900～1700 nm的高光谱图像，采用GB 5009.3-2016中的直接干燥法测定每种大豆籽粒样本的水分含量。基于联合X-Y距离的样本划分法按照4:1的比例划分样品，建立校正集和预测集。采用基线校正、移动平均、Savitzky-Golay滤波、归一化、标准正态变量变换、多元散射校正法、一阶导数、去卷积8种算法方法对原始光谱数据进行预处理，基于连续投影算法和竞争性自适应重加权算法提取特征波长，最后建立基于特征波长的偏最小二乘回归的大豆籽粒含水率预测模型。结果表明，最优模型预测集相关系数和均方根误差分别为 0.92和 0.2371。水分光谱反演模型可以准确快速无损预测大豆籽粒含水率，从而制定大豆机收时间，提升大豆收获、存储、加工品质。