Uniformity Detection for Straws Based on Overlapping Region Analysis

Junteng Ma, Feng Wu, Huanxiong Xie, Fengwei Gu, Hongchen Yang and Zhichao Hu
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Junteng Ma: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Feng Wu: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Huanxiong Xie: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Fengwei Gu: Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Hongchen Yang: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Zhichao Hu: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China

Agriculture, 2022, vol. 12, issue 1, 1-18

Abstract: 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.

Keywords: uniformity detection; morphological theory; threshold segmentation; overlapping region analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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