Identification and localisation of multiple weeds in grassland for removal operation

Jinjin Wang; Xiaopeng Yao; Bao Kha Nguyen

doi:10.1117/12.2644281

12 October 2022 Identification and localisation of multiple weeds in grassland for removal operation

Jinjin Wang, Xiaopeng Yao, Bao Kha Nguyen

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123420Z (2022) https://doi.org/10.1117/12.2644281
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Weeds are a common issue in agriculture. Image-based weed identification has regained popularity in recent years as computing power increases. Researchers have successfully applied weed detection in the crop field and have combined the sensor (e.g.camera) and mechanical such as robotic weeders to get the location of the weeds. Meanwhile, many studies also have been conducted on the two classifications between grass and weed. However, there is no excellent and comprehensive weed dataset in reality because weeds are always similar and difficult to obtain by non-specialists. Moreover, it is challenging to identify weeds from grasslands for their similar colors, sizes, and shapes. We investigate three weeds (Bitter Gentian, Hawk's Beard, Pedunculate) relatively common in grasslands. Then, we select the typical grassland dominated by the above weeds for data collection. A natural and effective dataset is built and has generality in the scene of actual grassland. Secondly, we extract image features, including Color, Histogram, and orientation gradient histogram (HOG), and make various combinations to accurately and comprehensively reflect the actual characteristics of weeds. Thirdly, we propose a "core zone" algorithm to locate the weeds. The algorithm mainly adopts technology in image processing, such as threshold segmentation and morphological transformations. Experiments show that our binary classifier is more accurate than the comparison method, and the accuracy of the multi-classifier is also high. In addition, the algorithm for weeds location is more efficient than the comparative method.

Citation Download Citation

Jinjin Wang, Xiaopeng Yao, and Bao Kha Nguyen "Identification and localisation of multiple weeds in grassland for removal operation", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123420Z (12 October 2022); https://doi.org/10.1117/12.2644281

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KEYWORDS

RGB color model

Image processing

Binary data

Feature extraction

Image classification

Machine learning

Robotics

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