Image detection and verification of visual navigation route during cotton field management period

Jingbin Li,Bingqi Chen,Rongguang Zhu

doi:10.25165/j.ijabe.20181106.3976

Abstract

In order to meet the actual operation demand of visual navigation during cotton field management period, image detection algorithm of visual navigation route during this period was investigated in this research. Firstly, for the operation images under natural environment, the approach of color component difference, which is applicable for cotton field management, was adopted to extract the target characteristics of different regions inside and outside cotton field. Secondly, the median filtering method was employed to eliminate noise in the images and realize smoothing process of the images. Then, according to the regional vertical cumulative distribution graph of the images, the boundary characteristic of the cotton seedling region was obtained and the central position of the cotton seedling row was determined. Finally, the detection of the candidate points cluster was realized, and the navigation route was extracted by Hough transformation passing the known point. The testing results showed that the algorithms could rapidly and accurately detect the navigation route during cotton field management period. And the average processing time periods for each frame image at the emergence, strong seedling, budding and blooming stages were 41.43 ms, 67.83 ms, 68.80 ms and 74.05 ms, respectively. The detection has the advantage of high accuracy, strong robustness and fast speed, and is simultaneously less vulnerable to interference from external environment, which satisfies the practical operation requirements of cotton field management machinery. Keywords: visual navigation, route detection, Hough transformation passing the known point, cotton field management period DOI: 10.25165/j.ijabe.20181106.3976 Citation: Li J B, Zhu R G, Chen B Q. Image detection and verification of visual navigation route during cotton field management period. Int J Agric & Biol Eng, 2018; 11(6): 159–165.

Highlights

Visual navigation is one common way of automatic navigation in agriculture, and has become a research hotspot in the field of automatic navigation
Many research achievements have been acquired in detection methods of navigation route in China and abroad, and these achievements mainly focus on detection of crops, such as rice, wheat, and corn, involving operation processes of cultivating, crop protection, sowing, and harvesting
The detection approaches mainly include: the method of extracting crop row based on the Hough transformation and obtaining parameters of crop row and navigation[1,2,3]; the procedure of detecting crops and obtaining their central line[4,5]; the technique of separating crop and background by filtering characteristic values and obtaining crop central line by robust linear fitting[6]; the method of extracting central line of rice seedling row based on color model and nearest neighbor clustering[7]; the approach of implementing rotary projection algorithm to image ROIs by angle enumeration[8]; the method of detecting row center of crop at early stage based on least squares method[9]; the technique of using the classification algorithm based on horizontal line scanning to solve

Summary

Introduction

Visual navigation is one common way of automatic navigation in agriculture, and has become a research hotspot in the field of automatic navigation. Many research achievements have been acquired in detection methods of navigation route in China and abroad, and these achievements mainly focus on detection of crops, such as rice, wheat, and corn, involving operation processes of cultivating, crop protection, sowing, and harvesting. Chen et al.[11,12,13,14,15,16,17] has thoroughly studied the visual system of the rice transplanting robot and the route detection of rice field management and farming robot. Li et al.[18,19] studied the machine vision detection of navigation route of the cotton planter and harvester. At present, few detection approaches for navigation route during cotton field management period in China have been reported

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