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Article

Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine

1
Department of Information Science, Xi’an University of Technology, Xi’an 710048, China
2
Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China
3
Zhengzhou Cotton & Jute Engineering Technology and Design Research Institute, Zhengzhou 451162, China
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(1), 212; https://doi.org/10.3390/s21010212
Received: 31 October 2020 / Revised: 14 December 2020 / Accepted: 28 December 2020 / Published: 31 December 2020
(This article belongs to the Special Issue Sensing Technologies for Agricultural Automation and Robotics)
Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing. View Full-Text
Keywords: precision spraying; precise fertilization; multi-feature; weed and corn seedling detection; support vector machine; rotation invariant LBP; Gabor feature; co-occurrence matrix precision spraying; precise fertilization; multi-feature; weed and corn seedling detection; support vector machine; rotation invariant LBP; Gabor feature; co-occurrence matrix
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MDPI and ACS Style

Chen, Y.; Wu, Z.; Zhao, B.; Fan, C.; Shi, S. Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine. Sensors 2021, 21, 212. https://doi.org/10.3390/s21010212

AMA Style

Chen Y, Wu Z, Zhao B, Fan C, Shi S. Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine. Sensors. 2021; 21(1):212. https://doi.org/10.3390/s21010212

Chicago/Turabian Style

Chen, Yajun, Zhangnan Wu, Bo Zhao, Caixia Fan, and Shuwei Shi. 2021. "Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine" Sensors 21, no. 1: 212. https://doi.org/10.3390/s21010212

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