A deep learning-based framework for accurate identification and crop estimation of olive trees

Over the last several years, olive cultivation has grown throughout the Mediterranean countries. Among them, Spain is the world's leading producer of olives. Due to its high economic significance, it is in the best interest of these countries to maintain the crop spread and its yield. Manual enumeration of trees over such extensive fields is impractical and humanly infeasible. There are several methods presented in the existing literature; nonetheless, the optimal method is of greater significance. In this paper, we propose an automated method of olive tree detection as well as crop estimation. The proposed approach is a two-step procedure that includes a deep learning-based classification model followed by regression-based crop estimation. During the classification phase, the foreground tree information is extracted using an enhanced segmentation approach, specifically the K-Mean clustering technique, followed by the synthesis of a super-feature vector comprised of statistical and geometric features. Subsequently, these extracted features are utilized to estimate the expected crop yield. Furthermore, the suggested method is validated using satellite images of olive fields obtained from Google Maps. In comparison with existing methods, the proposed method contributed in terms of novelty and accuracy, outperforming the rest by an overall classification accuracy of 98.1% as well as yield estimate with a root mean squared error of 0.185 respectively.