Identifying Agricultural Crops with Similar Spectral Properties Using Machine Learning Classifiers and SenseFly eBee SQ Multispectral UAV Images

This study investigates the use of vegetation indices for accurately identifying crops with similar spectral characteristics as grapes, apricots, tomatoes, wheat, and clover for enhancing crop monitoring and management. A 59-hectare area in Karakaya Village, located in the Üzümlü district of Erzincan Province, Türkiye, was selected as the study area. This area contains crops with varying textures, object height, and spectral characteristics. In the study, multispectral (MS) images were acquired using the SenseFly eBee SQ unmanned aerial vehicle (UAV), and subsequently processed to generate an orthophoto, digital terrain model (DTM), and digital surface model (DSM). Fifteen vegetation indices, Gabor texture features, and object heights were integrated into MS bands. Crop classification was performed using two high-accuracy machine learning algorithms: Random Forest (RF) and Support Vector Machine (SVM). According to the overall classification accuracy results, the use of vegetation indices improved classification accuracy by 9% for RF and 5% for SVM. Incorporating Gabor texture features with the topperforming indices (MACARI1, OSAVI, ADVI, and DVI) further increased accuracy to 20% for RF and 12% for SVM. Additionally, including object height alongside the indices and Gabor features resulted in further accuracy gainsof 10% and 11% for RF and SVM, respectively. F1-score, specificity, and accuracy analyses, along with various kappa statistics, also the significant improvements in classification performance. According to the McNemar test, the χ² values comparing orthophoto images with those incorporating indices, texture, and object height ranged from 6. 353 to 35. 556 for RF, and from 7. 220 to 11. 021 for SVM. Since all χ² values exceeded 3. 84, the results indicate statistically significant improvements in the classification accuracy at the 95% confidence interval.