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Planting Age Identification and Yield Prediction of Apple Orchard Using Time-Series Spectral Endmember and Logistic Growth Model

文献类型： 外文期刊

作者： Gao, Xiang 1 ; Han, Wenchao 1 ; Hu, Qiyuan 1 ; Qin, Yuting 1 ; Wang, Sijia 1 ; Lun, Fei 1 ; Sun, Jing 2 ; Wu, Jiechen 3 ; Xiao, Xiao 4 ; Lan, Yang 5 ; Li, Hong 6 ;

作者机构： 1.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China

2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China

3.KTH Royal Inst Technol, Dept Sustainable Dev Environm Sci & Engn SEED, S-10044 Stockholm, Sweden

4.China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China

5.UCL, Bartlett Sch Environm Energy & Resources, London WC1E 6BT, England

6.Beijing Acad Agr & Forestry Sci, Inst Plant Nutr & Resources, Beijing 100097, Peoples R China

关键词： apple yield; logistic growth model; planting age; spectral endmember; BP neural network

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 3 期

页码：

收录情况： SCI

摘要： In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise planting age identification and yield prediction are indispensable for the apple market in terms of sustainable supply, price regulation, and planting management. The planting age of apple trees significantly determines productivity, quality, and yield. Therefore, we integrated the time-series spectral endmember and logistic growth model (LGM) to accurately identify the planting age of apple orchard, and we conducted planting age-driven yield prediction using a neural network model. Firstly, we fitted the time-series spectral endmember of green photosynthetic vegetation (GV) with the LGM. By using the four-points method, the environmental carrying capacity (ECC) in the LGM was available, which serves as a crucial parameter to determine the planting age. Secondly, we combined annual planting age with historical apple yield to train the back propagation (BP) neural network model and obtained the predicted apple yields for 12 counties. The results show that the LGM method can accurately estimate the orchard planting age, with Mean Absolute Error (MAE) being 1.76 and the Root Mean Square Error (RMSE) being 2.24. The strong correlation between orchard planting age and apple yield was proved. The results of planting age-driven yield prediction have high accuracy, with the MAE up to 2.95% and the RMSE up to 3.71%. This study provides a novel method to accurately estimate apple orchard planting age and yields, which can support policy formulation and orchard planning in the future.

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