Integrating AI with hexacopter UAVs for precision agriculture: Current applications and future outlook

Abstract

The integration of AI with hexacopter-based UAVs enables high-resolution crop imaging and automated mapping. This study proposes a Hybrid AE–ViT framework, optimized using CoatiOA, for UAV-based crop health classification to distinguish healthy and weedy rice. DJI Mavic 3 Multispectral RGB and multispectral images undergo denoising, augmentation, standardization, and patch preprocessing. High-resolution RGB imagery captures textural and structural crop features, while multispectral bands (green, red, red-edge, and near-infrared) encode spectral and vegetation health information, enhancing model robustness and accuracy under varying field conditions. The Autoencoder performs spectral–spatial representation localization, while the Vision Transformer (ViT) identifies global contextual dependencies, with CoatiOA tuning fusion and network parameters. Testing confirms excellent generalization, achieving 98.4% accuracy, 1.000 precision, 96.7% recall, 0.983 F1-score, 0.9936 AUC, and 0.9956 Average Precision. The confusion matrix shows 572 healthy plants and 563 weedy rice plants correctly classified, with minimal misclassifications and a 0.000 False Positive Rate. Additionally, GPS-based geo-mapping generates spatial health distribution maps, supporting precise field-level decision-making. These results demonstrate the AE–ViT model's reliability, strength, and scalability for UAV-powered crop monitoring in precision agriculture.