Automated olive grove classification and tree counting in very high resolution aerial imagery using deep learning

The economic significance of olive trees has fueled a growing interest in the development of advanced and efficient technologies specifically designed for olive cultivation. A key focus within this area has been the use of remote sensing technologies. By integrating remote sensing with Deep Learning we developed a robust algorithm for olive grove classification and tree counting. Our automated procedure uses Very High-Resolution RGB aerial imagery and includes a land classification step based on EfficientNet to distinguish olive groves from a generic land type, followed by an automatic tree counting process leveraging the YOLO algorithm.We evaluated our method on an open dataset of aerial VHR RGB images covering 443.8 km2in Southern Italy. For the classification task, the proposed model achieved a Cohen's κ of 0.982 and an overall accuracy of 99% at the parcel level. For tree detection and counting, YOLO reached a median F1 score of 91.5%, with a precision of 93.0%, recall of 90.5%, and a mean absolute error of 11.4 trees per hectare (R2=0.90[jls-end-space/]).By leveraging state-of-the-art Deep Learning algorithms, our methodology marks a significant advancement in precision agriculture for olive orchards, offering notable improvements over current approaches.