Comparative analysis of mature tomato detection by feature extraction and machine learning for autonomous greenhouse robots

Abstract

Accurate detection of tomatoes grown in greenhouses is important for timely harvesting. In this way, it is ensured that mature tomatoes are collected by distinguishing them from the unripe ones. Insufficient light, occlusion, and overlapping adversely affect the detection of mature tomatoes. In addition, it is time consuming for people to detect mature tomatoes at certain periods in large greenhouses. For these reasons, high-performance automatic detection of tomatoes by greenhouse robots has become an increasingly studied area today. In this paper, two feature extraction methods, histogram of oriented gradients (HOG) and local binary patterns (LBP), which are effective in object recognition, and two important and commonly used classifiers of machine learning, support vector machines (SVM) and k-nearest neighbor (kNN), are comparatively used to detect and count tomatoes. The HOG and LBP features are classified separately and together by SVM or kNN, and the success of each case are compared. Performance of the detection is improved by eliminating false positive results at the postprocessing stage using color information.