Evaluating the Canopy Chlorophyll Density of Maize at the Whole Growth Stage Based on Multi-Scale UAV Image Feature Fusion and Machine Learning Methods

Lili Zhou, Chenwei Nie, Tao Su, Xiaobin Xu, Yang Song, Dameng Yin, Shuaibing Liu, Yadong Liu, Yi Bai, Xiao Jia and Xiuliang Jin ()
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Lili Zhou: School of Spatial Information and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, China
Chenwei Nie: National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572025, China
Tao Su: School of Spatial Information and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, China
Xiaobin Xu: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Yang Song: National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572025, China
Dameng Yin: National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572025, China
Shuaibing Liu: National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572025, China
Yadong Liu: National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572025, China
Yi Bai: National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572025, China
Xiao Jia: National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572025, China
Xiuliang Jin: National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572025, China

Agriculture, 2023, vol. 13, issue 4, 1-22

Abstract: Maize is one of the main grain reserve crops, which directly affects the food security of the country. It is extremely important to evaluate the growth status of maize in a timely and accurate manner. Canopy Chlorophyll Density (CCD) is closely related to crop health status. A timely and accurate estimation of CCD is helpful for managers to take measures to avoid yield loss. Thus, many methods have been developed to estimate CCD with remote sensing data. However, the relationship between the CCD and the features used in these CCD estimation methods at different growth stages is unclear. In addition, the CCD was directly estimated from remote sensing data in most previous studies. If the CCD can be accurately estimated from the estimation results of Leaf Chlorophyll Density (LCD) and Leaf Area Index (LAI) remains to be explored. In this study, Random Forest (RF), Support Vector Machines (SVM), and Multivariable Linear Regression (MLR) were used to develop CCD, LCD, and LAI estimation models by integrating multiple features derived from unmanned aerial vehicle (UAV) multispectral images. Firstly, the performances of the RF, SVM, and MLR trained over spectral features (including vegetation indices and band reflectance; dataset I), texture features (dataset II), wavelet coefficient features (dataset III), and multiple features (dataset IV, including all the above datasets) were analyzed, respectively. Secondly, the CCD P was calculated from the estimated LCD and estimated LAI, and then the CCD was estimated based on multiple features and the CCD P was compared. The results show that the correlation between CCD and different features is significantly different at every growth stage. The RF model trained over dataset IV yielded the best performance for the estimation of LCD, LAI, and CCD (R 2 values were 0.91, 0.97, and 0.97, and RMSE values were 6.59 μg/cm 2 , 0.35, and 24.85 μg/cm 2 ). The CCD directly estimated from dataset IV is slightly closer to the ground truth CCD than the CCD P (R 2 = 0.96, RMSE = 26.85 μg/cm 2 ) calculated from LCD and LAI. The results indicated that the CCD of maize can be accurately estimated from multiple multispectral image features at the whole growth stage, and both CCD estimation strategies can be used to estimate the CCD accurately. This study provides a new reference for accurate CCD evaluation in precision agriculture.

Keywords: machine learning; canopy chlorophyll density; multi-scale feature fusion; maize (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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