作物害虫图像智能识别方法

doi:10.6048/j.issn.1001-4330.2023.12.013

新疆农业科学 ›› 2023, Vol. 60 ›› Issue (12): 2973-2981.DOI: 10.6048/j.issn.1001-4330.2023.12.013

作物害虫图像智能识别方法

李雨晴(), 陈燕红(), 李永可, 肖天赐, 李清源

新疆农业大学计算机与信息工程学院,乌鲁木齐 830052

收稿日期:2023-04-10 出版日期:2023-12-20 发布日期:2024-01-03
通信作者: 陈燕红(1979-),女,新疆乌鲁木齐人,副教授,硕士,研究方向为视觉-语言智能表征学习,(E-mail)cyh@xjau.edu.cn
作者简介:李雨晴(1998-),女,新疆乌鲁木齐人,硕士研究生,研究方向为计算机视觉,(E-mail)943655041@qq.com
基金资助:
新疆维吾尔自治区自然科学基金面上项目(2019D01A50);新疆维吾尔自治区重大科技专项课题(2020A01002-4-1)

Image intelligent recognition method of crop pests

LI Yuqing(), CHEN Yanhong(), LI Yongke, XIAO Tianci, LI Qingyuan

School of Computer and Information Engineerins,Xinjiang Agricultural University, Urumqi 830052, China

Received:2023-04-10 Online:2023-12-20 Published:2024-01-03
Correspondence author: CHEN Yanhong(1979-), female, native place: Urumqi, Xinjiang. double master, associate professor, research field: Visual-Linguistic Intelligence Representation Learning,(E-mail)cyh@xjau.edu.cn
Supported by:
The General Program Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region(2019D01A50);Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region(2020A01002-4-1)

摘要/Abstract

摘要：

【目的】针对作物害虫数据集样本较少、现有单一模型在作物害虫识别上的准确率不高以及泛化能力较差的问题,提出一种基于迁移学习与多模型集成的害虫识别模型。【方法】在大规模公开作物害虫数据集IP102上进行试验,使用迁移学习单独训练6个深层神经网络,选择识别性能较好的EfficientNet、Vision Transformer、Swin Transformer和ConvNeXt进行组合,采用不同策略集成预测结果。【结果】提出的基于迁移学习与多模型集成方法的识别准确率达到75.75%,比性能最好的单模型ConvNeXt提高了1.34%,与目前该数据集上最优算法(CA-EfficientNet)的性能相比,识别准确率高出了6.3%。【结论】害虫图像智能识别模型具有较好的稳定性与泛化能力。

关键词: 害虫识别; IP102数据集; 迁移学习; 集成学习; 投票法

Abstract:

【Objective】Aiming at the problems of few crop pest dataset samples, low accuracy of existing single model in crop pest identification and poor generalization ability, a pest identification model based on transfer learning and multi-model integration is proposed.【Methods】Experiments were carried out on the large-scale public crop pest dataset IP102. In this study, transfer learning is used to train 6 deep neural networks separately, and the combination of EfficientNet, Vision Transformer, Swin Transformer and ConvNeXt with better recognition performance was selected, and then different strategies were used to integrate the prediction results.【Results】The results showed that the recognition accuracy of the proposed method based on transfer learning and multi-model integration reached 75.75%, which was 1.34% higher than that of the best-performing single-model ConvNeXt, and was comparable to the current compared with the performance of the optimal algorithm (CA-EfficientNet) on the dataset, the recognition accuracy was 6.3% higher, 【Conclusion】It has better stability and generalization ability.

Key words: pest identification; IP102 dataset; transfer learning; ensemble learning; voting method

中图分类号:

TP391.41
S41

李雨晴, 陈燕红, 李永可, 肖天赐, 李清源. 作物害虫图像智能识别方法[J]. 新疆农业科学, 2023, 60(12): 2973-2981.

LI Yuqing, CHEN Yanhong, LI Yongke, XIAO Tianci, LI Qingyuan. Image intelligent recognition method of crop pests[J]. Xinjiang Agricultural Sciences, 2023, 60(12): 2973-2981.

图/表 8

参考文献 23

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模型 Model	训练集准确率 Training set accuracy(%)	验证集准确率 Verification set accuracy(%)
EN	85.10	74.63
SN	52.33	54.19
MN	73.51	68.79
CN	91.19	74.91
VT	98.58	74.12
ST	84.64	73.27
平均准确率 Average accuracy	80.88	69.99

模型 Model	训练集准确率 Training set accuracy(%)	验证集准确率 Verification set accuracy(%)
EN	85.10	74.63
SN	52.33	54.19
MN	73.51	68.79
CN	91.19	74.91
VT	98.58	74.12
ST	84.64	73.27
平均准确率 Average accuracy	80.88	69.99

模型 Model	准确率 Accuracy(%)	F₁值 (%)
CN	74.41	67.84
EN	71.31	63.47
VT	62.35	51.83
ST	73.27	66.08

模型 Model	准确率 Accuracy(%)	F₁值 (%)
CN	74.41	67.84
EN	71.31	63.47
VT	62.35	51.83
ST	73.27	66.08

组合方式 Composition	相对多数投票法 Relative majority voting method		加权投票法Weighted voting method
	相对多数投票法 Relative majority voting method		(A)		(B)		(C)
	准确率 Accuracy(%)	F₁值 (%)	准确率 Accuracy(%)	F₁值 (%)	准确率 Accuracy(%)	F₁值 (%)	准确率 Accuracy(%)	F₁值 (%)
CN+ST+EN	75.16	68.37	75.09	68.53	-	-	75.37	68.64
CN+ST+VT	74.91	68.23	75.03	68.50	-	-	75.27	68.73
CN+EN+VT	74.00	66.81	74.73	68.04	74.33	67.16	74.73	68.04
EN+VT+ST	73.76	66.54	74.00	66.70	74.00	66.70	73.84	66.70
CN+EN+VT+ST	75.56	68.81	75.67	68.85	75.70	68.87	75.75	69.13

作物害虫图像智能识别方法

Image intelligent recognition method of crop pests

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图/表 8

参考文献 23

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Metrics

所用模型 The model used	准确率 Accuracy (%)	F₁值 (%)
FusionSum Densenet^[19]	61.93	59.20
SMPEnsemble^[20]	66.21	64.44
GAEnsemble^[20]	67.13	65.76
CA-EfficientNet^[21]	69.45	63.06
CRN^[22]	70.42	-
文献^[23]	73.46	72.90
CN	74.41	67.84
CN+EN+VT+ST	75.56	68.81
CN+EN+VT+ST(C)	75.75	69.13