Estimating the SPAD of Litchi in the Growth Period and Autumn Shoot Period Based on UAV Multi-Spectrum
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Published:2023-12-17
Issue:24
Volume:15
Page:5767
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Xie Jiaxing12, Wang Jiaxin1, Chen Yufeng1, Gao Peng1, Yin Huili1, Chen Shiyun1, Sun Daozong12, Wang Weixing3, Mo Handong1, Shen Jiyuan4, Li Jun5ORCID
Affiliation:
1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China 2. Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou 510642, China 3. Zhujiang College, South China Agricultural University, Guangzhou 510900, China 4. College of Horticulture, South China Agricultural University, Guangzhou 510642, China 5. College of Engineering, South China Agricultural University, Guangzhou 510642, China
Abstract
The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables.
Funder
Co-constructing Cooperative Project on Agricultural Sci-tech of New Rural Development Research Institute of South China Agricultural University China Agriculture Research System of MOF and MARA, China Guangdong Provincial Special Fund for Modern Agriculture Industry Technology Innovation Teams, China Key-Area Research and Development Program of Guangdong Province Guangdong Science and Technology Innovation Cultivation Special Fund Project for College Students (“Climbing Program” Special Fund), China Innovation and Entrepreneurship Training Program for College Students
Subject
General Earth and Planetary Sciences
Reference47 articles.
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