Corn yield prediction in site-specific management zones using proximal soil sensing, remote sensing, and machine learning approach

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Bantchina B. B., Qaswar M., ARSLAN S., ULUSOY Y., GÜNDOĞDU K. S., TEKİN Y., ...Daha Fazla

Computers and Electronics in Agriculture, cilt.225, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 225
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.compag.2024.109329
  • Dergi Adı: Computers and Electronics in Agriculture
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Food Science & Technology Abstracts, INSPEC, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Corn yield prediction, Machine learning, Management zones, Proximal soil sensing, Remote sensing, Variable rate fertilization
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

The integration of advanced technologies, such as soil proximal sensing, remote sensing, and machine learning, has revolutionized agricultural practices, particularly for corn yield prediction. This interdisciplinary approach harnesses the power of cutting-edge sensors to gather high-resolution data on soil conditions coupled with remote sensing technologies that provide a comprehensive view of crop health and environmental factors. This study aimed to evaluate the feasibility of accurately predicting corn (Zea mays) yield at the management zones (MZs) level using the fusion of visible and near-infrared spectroscopy (Vis-NIRS)-derived soil properties, remote sensing-derived crop spectral indices, and machine learning algorithms. Clustering analysis was used to develop MZs to implement variable-rate nitrogen fertilization (VRNF) in a drip-irrigated corn field. Site-specific models to forecast corn yield at the MZs level were developed using Sentinel 2A-derived spectral indices and machine learning regression algorithms. Partial least squares Vis-NIR spectral regression modelling for MZs development achieved high accuracy in terms of the coefficient of determination (R2) which was ranged from 0.60 to 0.99 in cross-validation and from 0.52 to 0.78 in online validation. The developed corn yield prediction models demonstrated moderate efficacy, as evidenced by the R2 values ranging from 0.50 to 0.71. Further research should include supplementary spectral crop canopy indices and the application of alternative deep and machine learning approaches to improve the accuracy of the prediction models.