Wheat Lodging Direction Detection for Combine Harvesters Based on Improved K-Means and Bag of Visual Words

Autor: Qian Zhang, Qingshan Chen, Lizhang Xu, Xiangqian Xu, Zhenwei Liang
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Agronomy, Vol 13, Iss 9, p 2227 (2023)
Druh dokumentu: article
ISSN: 13092227
2073-4395
16938593
DOI: 10.3390/agronomy13092227
Popis: For the inconsistent lodging of wheat with dense growth and overlapped organs, it is difficult to detect lodging direction accurately and quickly using vehicle vision for harvesters. Therefore, in this paper, the k-means algorithm is improved by designing a validity evaluation function, selecting initial clustering centers by distance, constructing a multidimensional feature vector, and simplifying calculations using triangle inequality. An adaptive image grid division method based on perspective mapping and inverse perspective mapping with a corrected basic equation is proposed for constructing a dataset of wheat lodging directions. The improved k-means algorithm and direction dataset are used to construct a bag of visual words. Based on scale-invariant feature transform, pyramid word frequency, histogram intersection kernel, and support vector machine, the wheat lodging directions were detected in the grid. The proposed method was verified through experiments with images acquired on an intelligent combine harvester. Compared with single-level word frequencies with existing and improved k-means, the mean accuracy of wheat lodging direction detection by pyramid word frequencies with improved k-means increased by 6.71% and 1.11%, respectively. The average time of detection using the proposed method was 1.16 s. The proposed method can accurately and rapidly detect wheat lodging direction for combine harvesters and further realize closed-loop control of intelligent harvesting operations.
Databáze: Directory of Open Access Journals
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