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Open AccessArticle

Weed Density Detection Method Based on Absolute Feature Corner Points in Field

1
College of Information and Technology, JiLin Agricultural University, Changchun 130118, China
2
Department of Biological Systems Engineering, Centre for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA 99350, USA
3
Changchun Institute of Engineering and Technology, Changchun 130117, China
*
Authors to whom correspondence should be addressed.
Agronomy 2020, 10(1), 113; https://doi.org/10.3390/agronomy10010113
Received: 18 November 2019 / Revised: 13 December 2019 / Accepted: 7 January 2020 / Published: 13 January 2020
(This article belongs to the Section Weed Science and Weed Management)
Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations. View Full-Text
Keywords: weed detection; absolute feature corner point; variable spraying; image processing weed detection; absolute feature corner point; variable spraying; image processing
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MDPI and ACS Style

Xu, Y.; He, R.; Gao, Z.; Li, C.; Zhai, Y.; Jiao, Y. Weed Density Detection Method Based on Absolute Feature Corner Points in Field. Agronomy 2020, 10, 113.

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