数字农科院2.0

Research on SPAD Inversion of Rice Leaves at a Field Scale Based on Machine Vision and Leaf Segmentation Techniques

文献类型： 外文期刊

作者： Bailin Yue;Yong Jin;Shangrong Wu;Jieyang Tan;Youxing Chen;Hu Zhong;Guipeng Chen;Yingbin Deng

作者机构：

关键词： feature optimization;leaf segmentation;machine vision;SPAD inversion;U-Net network model

期刊名称： Agriculture (Switzerland)

ISSN： 2077-0472

年卷期： 2025 年 15 卷 12 期

页码：

收录情况： SCIE（2025版）

摘要： Crop chlorophyll contents affect growth, and accurate assessment aids field management. SPAD (Soil Plant Analysis Development) values of leaves were mainly used to estimate chlorophyll content. Background interference affects the accuracy of SPAD value inversion. To address this issue, a rice leaf SPAD inversion method combining deep learning and feature selection is proposed. First, a leaf segmentation model based on U-Net was established. Then, the color features of leaf images were extracted. Seven color features highly correlated with SPAD were selected via the Pearson correlation coefficient and recursive feature elimination optimization. Finally, leaf SPAD inversion models based on random forest, support vector regression, BPNNs, and XGBoost were established. The results demonstrated that the U-Net could achieve accurate segmentation of leaves with a maximum mean intersection over union (MIoU) of 88.23. The coefficients of determination R2 between the anticipated and observed SPAD values of the four models were 0.819, 0.829, 0.896, and 0.721, and the root mean square errors (RMSEs) were 2.223, 2.131, 1.564, and 2.906. Through comparison, the method can accurately predict SPAD in different low-definition and saturation images, showing a certain robustness. It can offer technical support for accurate, nondestructive, and expedited evaluation of crop leaves’ chlorophyll content via machine vision.

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