Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data

Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen and Zhongchen Zhang ()
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Cong Liu: College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Lin Wang: College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Xuetong Fu: College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Junzhe Zhang: College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Ran Wang: College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Xiaofeng Wang: Baodong Town Agricultural Technology Extension Service Center, Hulin City 158407, China
Nan Chai: Agricultural Service Center, 856 Branch, Beidahuang Agricultural Co., Ltd., Hulin City 158418, China
Longfeng Guan: Agricultural Service Center, 856 Branch, Beidahuang Agricultural Co., Ltd., Hulin City 158418, China
Qingshan Chen: College of Agriculture, Northeast Agricultural University, Harbin 150030, China
Zhongchen Zhang: College of Agriculture, Northeast Agricultural University, Harbin 150030, China

Agriculture, 2025, vol. 15, issue 13, 1-26

Abstract: The chlorophyll index (CHI) is a crucial indicator for assessing the photosynthetic capacity and nutritional status of crops. However, traditional methods for measuring CHI, such as chemical extraction and handheld instruments, fall short in meeting the requirements for efficient, non-destructive, and continuous monitoring at the canopy level. This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. Canopy spectral data were acquired under nighttime conditions using a high-throughput phenotyping platform (HTTP) equipped with active light sources in a greenhouse environment. Three types of sensors—multispectral (MS), visible light (RGB), and chlorophyll fluorescence (ChlF)—were employed to collect data across different growth stages of rice, ranging from tillering to maturity. PCA and LASSO regression were applied for dimensionality reduction and feature selection of multi-source spectral variables. Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). The predictive performance of individual sensors (MS, RGB, and ChlF) and sensor fusion strategies was evaluated across multiple growth stages. The results demonstrated that sensor fusion models consistently outperformed single-sensor approaches. Notably, during tillering (TI), maturity (MT), and the full growth period (GP), fused models achieved high accuracy ( R 2 > 0.90, RMSE < 2.0). The fusion strategy also showed substantial advantages over single-sensor models during the jointing–heading (JH) and grain-filling (GF) stages. Among the individual sensor types, MS data achieved relatively high accuracy at certain stages, while models based on RGB and ChlF features exhibited weaker performance and lower prediction stability. Overall, the highest prediction accuracy was achieved during the full growth period (GP) using fused spectral data, with an R 2 of 0.96 and an RMSE of 1.99. This study provides a valuable reference for developing CHI prediction models based on nighttime multi-source spectral data.

Keywords: rice; chlorophyll index; nighttime spectral sensing; feature selection; machine learning (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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