Proximal hyperspectral sensing and PLSR for non-destructive estimation of leaf chlorophyll content in citrus during the fruit ripening Sensoriamento hiperespectral proximal e PLSR para a estimativa não destrutiva do teor de clorofila foliar em citros durante o amadurecimento dos frutos

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Çoşlu M., SÖNMEZ N. K.

Ciencia e Agrotecnologia, cilt.50, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1590/1413-7054202650006126
  • Dergi Adı: Ciencia e Agrotecnologia
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Directory of Open Access Journals
  • Anahtar Kelimeler: Precision agriculture, proximal sensing, SPAD, vegetation index
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Chlorophyll content is a key indicator of plant physiological status and photosynthetic activity; therefore, its rapid, non-destructive, and cost-effective assessment is of considerable importance for crop monitoring and management. This study aims to estimate the chlorophyll content of Satsuma mandarin and Valencia orange leaves using five wavelengths (blue, green, red, red-edge and near infrared) and ten different vegetation indices. The study was conducted during the fruit ripening period of citrus trees. Spectroradiometric and SPAD (The Soil Plant Analysis Development) measurements were performed within the scope of proximal remote sensing. Ten vegetation indices, including LCI, CI-G, GNDVI, NDVI, SR, TGI, VARI, NGRDI, NGBDI, and GLI, were derived from hyperspectral (HS) data obtained through spectroradiometric measurements. The statistical analyses estimated SPAD values by Partial Least Squares Regression (PLSR) analysis. According to the findings, the model developed for Satsuma mandarin achieved R², RMSE, and MAPE values of 0.89, 1.79, and 2.16%, respectively, while the corresponding values for the Valencia orange were 0.90, 1.60, and 2.01%. These results demonstrate that the relative chlorophyll content during fruit ripening period can be successfully estimated using HS data and the PLSR algorithm. The proposed approach may support rapid chlorophyll monitoring during fruit ripening, a critical period for nutrient management and orchard decision-making, thereby contributing to precision agriculture applications in citrus production.