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- Gao, XiangCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (author)
Planting Age Identification and Yield Prediction of Apple Orchard Using Time-Series Spectral Endmember and Logistic Growth Model
- Article/chapterEnglish2023
Publisher, publication year, extent ...
- 2023-01-21
- MDPI AG,2023
- printrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:kth-323336
- https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-323336URI
- https://doi.org/10.3390/rs15030642DOI
Supplementary language notes
- Language:English
- Summary in:English
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- QC 20230130
- 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 and genre
- 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
- apple yield
- logistic growth model
- planting age
- spectral endmember
- BP neural network
Added entries (persons, corporate bodies, meetings, titles ...)
- Han, WenchaoCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (author)
- Hu, QiyuanCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (author)
- Qin, YutingCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (author)
- Wang, SijiaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (author)
- Lun, FeiCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (author)
- Sun, JingInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China (author)
- Wu, JiechenKTH,Vatten- och miljöteknik(Swepub:kth)u1iq8nyf (author)
- Xiao, XiaoCollege of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China (author)
- Lan, YangThe Bartlett School of Environment, Energy and Resources, University College London, London WC1E 6BT, UK (author)
- Li, HongInstitute of Plant Nutrition and Resources, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China (author)
- College of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China (creator_code:org_t)
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- In:Remote Sensing: MDPI AG15:3, s. 642-6422072-4292
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