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30 April 2019

19 Pages

A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis †

,
and
1
Computer Software Institute, Weifang University of Science and Technology, Shouguang 262-700, China
2
Department of Electronics Engineering, Pusan National University, Busan 46241, Korea
*
Author to whom correspondence should be addressed.
†
This work is an extended version of the conference paper published in 2019 International Conference on Machine Learning and Computing (ICMLC) entitled ”A Robust Mature Tomato Detection in Greenhouse Scenes Using Machine Learning and Color Analysis”, Zhuhai, China, 22–24 February 2019.
Sensors2019, 19(9), 2023;https://doi.org/10.3390/s19092023
This article belongs to the Section Intelligent Sensors
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Abstract

An algorithm was proposed for automatic tomato detection in regular color images to reduce the influence of illumination and occlusion. In this method, the Histograms of Oriented Gradients (HOG) descriptor was used to train a Support Vector Machine (SVM) classifier. A coarse-to-fine scanning method was developed to detect tomatoes, followed by a proposed False Color Removal (FCR) method to remove the false-positive detections. Non-Maximum Suppression (NMS) was used to merge the overlapped results. Compared with other methods, the proposed algorithm showed substantial improvement in tomato detection. The results of tomato detection in the test images showed that the recall, precision, and F1 score of the proposed method were 90.00%, 94.41 and 92.15%, respectively.

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