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  • Gao, XiangCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (författare)

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

  • Artikel/kapitelEngelska2023

Förlag, utgivningsår, omfång ...

  • 2023-01-21
  • MDPI AG,2023
  • printrdacarrier

Nummerbeteckningar

  • 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

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

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Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • 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.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Han, WenchaoCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (författare)
  • Hu, QiyuanCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (författare)
  • Qin, YutingCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (författare)
  • Wang, SijiaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (författare)
  • Lun, FeiCollege of Land Science and Technology, China Agricultural University, Beijing 100193, China (författare)
  • Sun, JingInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China (författare)
  • Wu, JiechenKTH,Vatten- och miljöteknik(Swepub:kth)u1iq8nyf (författare)
  • Xiao, XiaoCollege of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China (författare)
  • Lan, YangThe Bartlett School of Environment, Energy and Resources, University College London, London WC1E 6BT, UK (författare)
  • Li, HongInstitute of Plant Nutrition and Resources, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China (författare)
  • 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)

Sammanhörande titlar

  • Ingår i:Remote Sensing: MDPI AG15:3, s. 642-6422072-4292

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