Uniformity Detection for Straws Based on Overlapping Region Analysis

Nowadays, the advanced comprehensive utilization and the complete prohibition of burning fully covered straws in croplands have become increasingly important in agriculture engineering. As a kind of direct straw-mulching method in China, conservation tillage with straw smashing is an effective method to reduce pollution and enhance fertility. In view of the high straw-returning yields, complicated manual operation, and the poor performance of straw detection with machine vision, this study introduces a novel form of uniformity detection for straws based on overlapping region analysis. An image-processing technology using a novel overlapping region analysis was proposed to overcome the inefficiency and low precision resulting from the manual identification of the straw uniformity. In this study, the debris in the gray map was removed according to region characteristics. Through using morphological theory with overlapping region analysis in low-density cases, straws of appropriate length can be identified and then uniformity detection can be accomplished. Compared with traditional threshold segmentation methods, the advantages of an accurate identification, fast operation, and high efficiency contribute to the better performance of the innovative overlapping region analysis. Finally, the proposed algorithm was verified through detecting the uniformity in low-density cases, with an average accuracy rate of 97.69%, providing a novel image recognition solution for automatic straw-mulching systems.