Crop and weed classification based on AutoML

Xuetao Jiang; Binbin Yong; Soheila Garshasbi; Jun Shen; Meiyu Jiang; Qingguo Zhou; Xuetao Jiang; Binbin Yong; Soheila Garshasbi; Jun Shen; Meiyu Jiang; Qingguo Zhou

doi:10.3934/aci.2021003

Applied Computing and Intelligence

2021, Volume 1, Issue 1: 46-60. doi: 10.3934/aci.2021003

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Research article

Crop and weed classification based on AutoML

1.
School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
2.
School of Computing and Information Technology, University of Wollongong, NSW 2522, Australia

Received: 10 October 2021 Accepted: 28 November 2021 Published: 07 December 2021

CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective functions are not thoroughly compatible with agricultural farming tasks as the corresponding models suffer from misclassifying crop to weed, often more likely than in other deep learning application domains. In this paper, we applied autonomous machine learning with a new objective function for crop and weed classification, achieving higher accuracy and lower crop killing rate (rate of identifying a crop as a weed). The experimental results show that our method outperforms state-of-the-art applications, for example, ResNet and VGG19.
- CNN,
- image classification,
- multi-models
Citation: Xuetao Jiang, Binbin Yong, Soheila Garshasbi, Jun Shen, Meiyu Jiang, Qingguo Zhou. Crop and weed classification based on AutoML[J]. Applied Computing and Intelligence, 2021, 1(1): 46-60. doi: 10.3934/aci.2021003

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Abstract

CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective functions are not thoroughly compatible with agricultural farming tasks as the corresponding models suffer from misclassifying crop to weed, often more likely than in other deep learning application domains. In this paper, we applied autonomous machine learning with a new objective function for crop and weed classification, achieving higher accuracy and lower crop killing rate (rate of identifying a crop as a weed). The experimental results show that our method outperforms state-of-the-art applications, for example, ResNet and VGG19.

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