Banana detection based on color and texture features in the natural environment

Banana detection by picking robots in outdoor conditions is difficult due to the color similarity with leaves and stems. A method of banana detection in the natural environment based on color and texture features was performed in this study by using a regular red-green-blue color camera. First, part of the background was removed in HSV color space by analyzing the relationship between the S color component and V color component; this saved detection time and improved the detection efficiency. Then, the banana area was found by adopting support vector machine with local binary pattern features and histogram of oriented gradient features of the banana. Single-feature and multi-feature fusion with different classifiers were compared to find the most suitable classification algorithm for banana detection. A validation set containing 4400 samples was used to evaluate the proposed classification algorithm. The precision and recall of banana detection were 100%. A total of 120 photos under different illumination conditions were selected as the test set. The average single-scale detection rate based on the proposed algorithm was 89.63%, the average execution time was 1.325 s, and the shortest execution time was 0.343 s. At last, the multi-scale detection method based on the proposed algorithm was discussed to improve the detection accuracy. The results showed that the developed method can be applied to the detection of banana in plantations under different illumination and occlusion conditions.