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

Bailin Yue, Yong Jin, Shangrong Wu (), Jieyang Tan, Youxing Chen, Hu Zhong, Guipeng Chen and Yingbin Deng
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Bailin Yue: School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
Yong Jin: School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
Shangrong Wu: State Key Laboratory of Efficient Utilization of Arable Land in China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Jieyang Tan: Institute of Agricultural Economics and Information, Hunan Academy of Agricultural Sciences, Changsha 410125, China
Youxing Chen: School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
Hu Zhong: State Key Laboratory of Efficient Utilization of Arable Land in China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Guipeng Chen: Institute of Agricultural Economics and Information, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
Yingbin Deng: Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China

Agriculture, 2025, vol. 15, issue 12, 1-24

Abstract: 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 R 2 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.

Keywords: SPAD inversion; machine vision; leaf segmentation; U-Net network model; feature optimization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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