Remotely Sensed Prediction of Rice Yield at Different Growth Durations Using UAV Multispectral Imagery

Shanjun Luo, Xueqin Jiang (), Weihua Jiao, Kaili Yang, Yuanjin Li and Shenghui Fang ()
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Shanjun Luo: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Xueqin Jiang: School of Information Science and Engineering, Shandong Agriculture University, Tai’an 271001, China
Weihua Jiao: Center for Agricultural and Rural Economic Research, Shandong University of Finance and Economics, Jinan 250014, China
Kaili Yang: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Yuanjin Li: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Shenghui Fang: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Agriculture, 2022, vol. 12, issue 9, 1-17

Abstract: A precise forecast of rice yields at the plot scale is essential for both food security and precision agriculture. In this work, we developed a novel technique to integrate UAV-based vegetation indices (VIs) with brightness, greenness, and moisture information obtained via tasseled cap transformation (TCT) to improve the precision of rice-yield estimates and eliminate saturation. Eight nitrogen gradients of rice were cultivated to acquire measurements on the ground, as well as six-band UAV images during the booting and heading periods. Several plot-level VIs were then computed based on the canopy reflectance derived from the UAV images. Meanwhile, the TCT-based retrieval of the plot brightness (B), greenness (G), and a third component (T) indicating the state of the rice growing and environmental information, was performed. The findings indicate that ground measurements are solely applicable to estimating rice yields at the booting stage. Furthermore, the VIs in conjunction with the TCT parameters exhibited a greater ability to predict the rice yields than the VIs alone. The final simulation models showed the highest accuracy at the booting stage, but with varying degrees of saturation. The yield-prediction models at the heading stage satisfied the requirement of high precision, without any obvious saturation phenomenon. The product of the VIs and the difference between the T and G (T − G) and the quotient of the T and B (T/B) was the optimum parameter for predicting the rice yield at the heading stage, with an estimation error below 7%. This study offers a guide and reference for rice-yield estimation and precision agriculture.

Keywords: yield estimation; rice; unmanned aerial vehicle (UAV); tasseled cap transformation; precision agriculture (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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