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

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

Gao, Xiang (author): College of Land Science and Technology, China Agricultural University, Beijing 100193, China

Han, Wenchao (author): College of Land Science and Technology, China Agricultural University, Beijing 100193, China

Hu, Qiyuan (author): College of Land Science and Technology, China Agricultural University, Beijing 100193, China

Qin, Yuting (author): College of Land Science and Technology, China Agricultural University, Beijing 100193, China

Wang, Sijia (author): College of Land Science and Technology, China Agricultural University, Beijing 100193, China

Lun, Fei (author): College of Land Science and Technology, China Agricultural University, Beijing 100193, China

Sun, Jing (author): Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Wu, Jiechen (author): KTH,Vatten- och miljöteknik

Xiao, Xiao (author): College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China

Lan, Yang (author): The Bartlett School of Environment, Energy and Resources, University College London, London WC1E 6BT, UK

Li, Hong (author): Institute of Plant Nutrition and Resources, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

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2023-01-21
2023
English.
In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 15:3, s. 642-642

Related links:: https://doi.org/10.3...; show more...; https://urn.kb.se/re...; https://doi.org/10.3...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

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.

Subject headings

NATURVETENSKAP -- Geovetenskap och miljövetenskap (hsv//swe)
NATURAL SCIENCES -- Earth and Related Environmental Sciences (hsv//eng)
TEKNIK OCH TEKNOLOGIER -- Naturresursteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Environmental Engineering (hsv//eng)
LANTBRUKSVETENSKAPER -- Lantbruksvetenskap, skogsbruk och fiske (hsv//swe)
AGRICULTURAL SCIENCES -- Agriculture, Forestry and Fisheries (hsv//eng)

Keyword

apple yield
logistic growth model
planting age
spectral endmember
BP neural network

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art (subject category)

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By the author/editor: Gao, Xiang; Han, Wenchao; Hu, Qiyuan; Qin, Yuting; Wang, Sijia; Lun, Fei; show more...; Sun, Jing; Wu, Jiechen; Xiao, Xiao; Lan, Yang; Li, Hong; show less...

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NATURAL SCIENCES: NATURAL SCIENCES; and Earth and Relate ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Environmental En ...

AGRICULTURAL SCIENCES: AGRICULTURAL SCI ...; and Agriculture Fore ...

Articles in the publication: Remote Sensing

By the university: Royal Institute of Technology

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