Image processing algorithms for in-field cotton boll detection in natural lighting conditions

Abstract

In developing countries, the cotton harvesting operation is currently being performed manually. Due to the monotonous nature of this task and the involvement of a considerable amount of labor, this operation becomes very tedious and costly. The harvesting robots can be a good alternative for the selective picking of cotton bolls from the field. In this study, an attempt has been made to develop the image processing algorithms for in-field cotton boll detection in natural lighting conditions for the cotton harvesting robot. Four image processing algorithms namely color difference, band ratio, YCbCr method, and chromatic aberration were proposed for the real-time segmentation of cotton bolls under natural outdoor light conditions. The performance of developed image processing algorithms was evaluated and the experimental results revealed that the chromatic aberration method outperforms as compared to other developed algorithms. The chromatic aberration method showed the highest identification rate of 91.05% with false positive and false negative rates of 6.99% and 4.88% respectively, among all the proposed algorithms. The highest sensitivity and specificity were found to be 81.31% and 97.53%, respectively, using the chromatic aberration method. Overall, the chromatic aberration approach demonstrated a very promising performance for in-field cotton bolls detection under natural lighting conditions which confirms its applicability for the robotic cotton harvesters.