Research and application on corn crop identification and positioning method based on Machine vision

Abstract

Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield. Therefore, the study explores corn identification and positioning methods based on machine vision. The ultra-green feature algorithm and maximum between-class variance method (OTSU) were used to segment maize corn, weeds, and land; the segmentation effect was significant and can meet the following shape feature extraction requirements. Finally, the identification and positioning of corn were achieved by morphological reconstruction and pixel projection histogram method. The experiment reveals that when a weeding robot travels at a speed of 1.6 km/h, the recognition accuracy can reach 94.1%. The technique used in this study is accessible for normal cases and can make a good recognition effect; the accuracy and real-time requirements of robot recognition are improved and reduced the calculation time.