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

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Detalles Bibliográficos
Publicado en:	Agriculture vol. 15, no. 13 (2025), p. 1425-1451
Autor principal:	Liu, Cong
Otros Autores:	Wang, Lin, Fu Xuetong, Zhang Junzhe, Wang, Ran, Wang, Xiaofeng, Chai Nan, Guan Longfeng, Chen, Qingshan, Zhang Zhongchen
Publicado:	MDPI AG
Materias:	China Physiology Light sources Sensors Measuring instruments Back propagation networks Feature selection Machine learning Crop diseases Chlorophyll Prediction models Canopies Chemical extraction Nitrogen Remote sensing Phenotyping Experiments Rice Fertilizers Multisensor fusion Measurement methods Accuracy Data acquisition Data collection Learning algorithms Nutritional status Vegetation Neural networks Support vector machines Nighttime Night Light Environmental
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Descripción
Resumen:	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 (R2 > 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 R2 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.
ISSN:	2077-0472
DOI:	10.3390/agriculture15131425
Fuente:	Agriculture Science Database