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Open AccessArticle

Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
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Agronomy 2020, 10(4), 590; https://doi.org/10.3390/agronomy10040590
Received: 18 March 2020 / Revised: 15 April 2020 / Accepted: 17 April 2020 / Published: 20 April 2020
(This article belongs to the Special Issue Precision Agriculture for Sustainability)
A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester. View Full-Text
Keywords: combine harvester; navigation path; cut-edge detection; ROI extraction combine harvester; navigation path; cut-edge detection; ROI extraction
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Zhang, Z.; Cao, R.; Peng, C.; Liu, R.; Sun, Y.; Zhang, M.; Li, H. Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision. Agronomy 2020, 10, 590.

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