Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

• Main Authors: Muhammad Waleed, Tai-Won Um, Aftab Khan, Umair Khan
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Remote Sensing
• Subjects: olive; image segmentation; image classification; centroid selection; jaccard analysis; very high-resolution imagery
• Online Access: https://www.mdpi.com/2072-4292/12/5/760

Olive cultivation over the past few years has spread across Mediterranean countries with Spain being the world’s largest olive producer among them. Because olives are a major part of the economy for such countries keeping records of their tree count and crop yield is of high significance. Manual counting of trees over such large areas is humanly infeasible. To address this problem, we propose an automatic method for the detection and enumeration of olive trees. The algorithm is a multi-step classification system comprising pre-processing, image segmentation, feature extraction, and classification. RGB satellite images were acquired from the Spanish territory and pre-processed to suppress the additive noise. The region of interest was then segmented from the pre-processed images using K-Means segmentation, through which statistical features were extracted and classified. Promising results were achieved for all classifiers, namely Naive Bayesian, Support Vector Machines (SVMs), Random Forest and Multi-Layer Perceptrons (MLPs), at various division ratios of data samples. In a comparison of all the classification algorithms, Random Forest outperformed the rest by an overall accuracy of 97.5% at the division ratio of 70 to 30 for training to testing.