Seed and Oil Yield Production of Safflower (Carthamus tinctorius L.) Using UAV-Based Multispectral Imaging and Machine Learning Algorithms

Creative Commons License

Bozdemir İ., Azizoğlu F., Azizoğlu G., Şatana A., Toprak A. N., Ünlükara A.

AGRICULTURE, cilt.16, sa.1566, ss.1-26, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 1566
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3390/agriculture16141566
  • Dergi Adı: AGRICULTURE
  • Derginin Tarandığı İndeksler: Natural Science Collection (ProQuest), Scopus, Science Citation Index Expanded (SCI-EXPANDED), Geobase, CAB Abstracts, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1-26
  • Erciyes Üniversitesi Adresli: Evet

Özet

The objective of this study was to predict seed and oil yields in safflower (Carthamus tinctorius L.) using UAV-based multispectral imagery and machine learning algorithms. The study was conducted during the 2024 growing season under varying irrigation levels, fertilization practices, and applications of plant growth-promoting rhizobacteria. The ex periment included four irrigation levels, fertilized and unfertilized conditions, and bacterial treatments consisting of Bacillus pumilus, Bacillus albus, their mixture, and a non-bacterial control. Sixty-two vegetation indices were calculated from multispectral images acquired during the harvest maturity period and used to predict seed and oil yields. To identify the most informative features, Mutual Information, Recursive Feature Elimination, and LASSO feature selection methods were applied; subsequently, Linear Regression, Decision Tree, Random Forest, Support Vector Regression, K-Nearest Neighbors, and XGBoost regression algorithms were compared. The results showed that Linear Regression combined with Mu tual Information-based feature selection was the most successful approach for predicting both seed and oil yields. According to the 5-fold cross-validation results, average values of AcademicEditor: JiangboLi Received: 26May2026 Revised: 19July2026 Accepted: 20July2026 Published: 22 July 2026 Copyright: ©2026bytheauthors. Licensee MDPI,Basel,Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY)license. R = 0.8600, MAE = 0.2870, and RMSE = 0.3765wereobtained for seed yield prediction, while average values of R = 0.8710, MAE = 0.0876, and RMSE = 0.1101 were obtained for oil yield prediction.