Estimating the leaf water content of Coffea arabica L. based on hyperspectral reflectance and dataset construction

Authors

  • Xiaogang Liu 1. Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China 2. Yunnan Provincial Field Scientific Observation and Research Station on Water-Soil-Crop System in Seasonal Arid Region, Kunming University of Science and Technology, Kunming 650500, China
  • Kailun Peng 1. Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
  • Shaomin Chen 1. Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China 2. Yunnan Provincial Field Scientific Observation and Research Station on Water-Soil-Crop System in Seasonal Arid Region, Kunming University of Science and Technology, Kunming 650500, China
  • Yunfei Tuo 3. College of Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China
  • Shuai Zhang 1. Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
  • Shuai Tan 1. Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China 2. Yunnan Provincial Field Scientific Observation and Research Station on Water-Soil-Crop System in Seasonal Arid Region, Kunming University of Science and Technology, Kunming 650500, China
  • Fangchuan Lou 1. Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
  • Jinxue Li 4. College of Biotechnology and Engineering, West Yunnan University, Lincang 677000, Yunnan, China
  • Zhenjia Chen 5. China Coffee Engineering Research Center, Dehong Normal University, Dehong 678400, Yunnan, China

DOI:

https://doi.org/10.25165/ijabe.v18i5.9067

Keywords:

spectral index, feature band, dataset, Coffea arabica L., leaf water content, machine learning

Abstract

Currently, irrigation decisions in coffee cultivation primarily rely on empirical knowledge, resulting in inefficient practices. Combining real-time leaf water content (LWC) data can help improve the accuracy of the irrigation planning. Spectral remote sensing is a fast, reliable, and non-invasive method to detect vegetation moisture content. In this study, a model to estimate the LWC of Coffea arabica L. was built using hyperspectral reflectance of the canopy under various irrigation levels. For this purpose, common spectral indices, two-band spectral indices [ratio spectral index (RSI); difference spectral index (DSI); and normalized difference spectral index (NDSI)], and three-band spectral indices were constructed. Feature bands were extracted using the successive projections algorithm (SPA). Optimal spectral indices were extracted using the correlation coefficient method, and the feature wavebands and spectral indices were combined into five datasets. These datasets were split into modeling and validation datasets by sample set partitioning based on the joint x-y distance (SPXY) algorithm. A linear model [partial least squares regression (PLSR)] and three non-linear models [support vector machine (SVM); extreme learning machine (ELM); back propagation artificial neural network (BPANN)] were built to estimate LWC of Coffea arabica L. The results indicated that the non-linear models surpassed the linear model. The accuracy was the highest when the modeling was performed using the dataset combination 5. Among various modeling methods, the predictive performance of ELM was the best (modeling dataset: R2=0.745, RMSE=2.241%, RRMSE=3.482%; validation dataset: R2=0.721, RMSE=2.142%, RRMSE=3.364%). ELM outperformed PLSR, SVM, and BPANN in LWC retrieval. The obtained results indicated that the dataset built by the combined use of different methods was superior to those from a single data source in accuracy. This study provides a scientific basis for the quantitative diagnosis of coffee tree water status, with significant implications for optimizing field irrigation management. Keywords: spectral index, feature band, dataset, Coffea arabica L., leaf water content, machine learning DOI: 10.25165/j.ijabe.20251805.9067 Citation: Liu X G, Peng K L, Chen S M, Tuo Y F, Zhang S, Tan S, et al. Estimating the leaf water content of Coffea arabica L. based on hyperspectral reflectance and dataset construction. Int J Agric & Biol Eng, 2025; 18(5): 287–297.

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Published

2025-10-27

How to Cite

Liu, X., Peng, K., Chen, S., Tuo, Y., Zhang, S., Tan, S., … Chen, Z. (2025). Estimating the leaf water content of Coffea arabica L. based on hyperspectral reflectance and dataset construction. International Journal of Agricultural and Biological Engineering, 18(5), 287–297. https://doi.org/10.25165/ijabe.v18i5.9067

Issue

Vol. 18 No. 5 (2025): IJABE

Section

Information Technology, Sensors and Control Systems

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