Classification and detection of insects from field images using deep learning for smart pest management: A systematic review

Li, Wenyong; Zheng, Tengfei; Yang, Zhankui; Li, Ming; Sun, Cheng; Yang, Xinting

doi:10.1016/j.ecoinf.2021.101460

Cited by 115 publications

(35 citation statements)

References 77 publications

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“…In relation to insect location, classification and counting, some works can be found. A comprehensive review of the use of deep learning based techniques for the classification and detection of pest is presented in [22]. Some years ago [3], proposes a method to count whiteflies in soybean leaves.…”

Section: Related Workmentioning

confidence: 99%

Multiclass insect counting through deep learning-based density maps estimation

Bereciartua-Pérez

Gómez

Picón

et al. 2023

Smart Agricultural Technology

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Section: Related Workmentioning

confidence: 99%

Multiclass insect counting through deep learning-based density maps estimation

Bereciartua-Pérez

Gómez

Picón

et al. 2023

Smart Agricultural Technology

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“…Their solution integrates many computer vision technologies to enhance numerous areas of picture quality and appropriateness. The extensive review [11] examines deep learning framework methodologies and applications in smart pest monitoring, with a focus on insect pest categorization and detection using field photos. The methodology and technical information created in insect pest classification and detection using deep learning are consolidated and distilled during multiple processing stages: picture collection, data preprocessing, and modeling strategies.…”

Section: Background Studymentioning

confidence: 99%

An Efficient System for Real-time Mobile Smart Device-based Insect Detection

Doan¹

2022

IJACSA

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In recent years, the rapid development of many pests and diseases has caused heavy damage to the agricultural production of many countries. However, it is difficult for farmers to accurately identify each type of insect pest, and yet they have used a large number of pesticides indiscriminately, causing serious environmental pollution. Meanwhile, spraying pesticides is very expensive, and thus developing a system to identify cropdamaging pests early will help farmers save a lot of money while also contributing to the development of sustainable agriculture. This paper presents a new efficient deep learning system for realtime insect image recognition on mobile devices. Our system achieved an accuracy of mAP@0.5 with the YOLOv5-S model of 70.5% on the 10 insect dataset and 42.9% on the IP102 largescale insect dataset. In addition, our system can provide more information to farmers about insects such as biological characteristics, distribution, morphology, and pest control measures. From there, farmers can take appropriate measures to prevent pests and diseases, helping reduce production costs and protecting the environment.

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“…Accurate insect recognition is increasingly important for warning the upcoming insect disasters and an effective way to prevent large-scale intrusion. The variance in morphology among different types of insects is relatively slight, and the same insect species hold different stages, including egg, larva, pupa and adult 1 . In other words, significant intra-class differences and large inter-species similarities make the recognition of insect pests challenging.…”

Section: Introductionmentioning

confidence: 99%

Insect recognition based on complementary features from multiple views

Hong

et al. 2023

Sci Rep

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Insect pest recognition has always been a significant branch of agriculture and ecology. The slight variance among different kinds of insects in appearance makes it hard for human experts to recognize. It is increasingly imperative to finely recognize specific insects by employing machine learning methods. In this study, we proposed a feature fusion network to synthesize feature presentations in different backbone models. Firstly, we employed one CNN-based backbone ResNet, and two attention-based backbones Vision Transformer and Swin Transformer to localize the important regions of insect images with Grad-CAM. During this process, we designed new architectures for these two Transformers to enable Grad-CAM to be applicable in such attention-based models. Then we further proposed an attention-selection mechanism to reconstruct the attention area by delicately integrating the important regions, enabling these partial but key expressions to complement each other. We only need part of the image scope that represents the most crucial decision-making information for insect recognition. We randomly selected 20 species of insects from the IP102 dataset and then adopted all 102 kinds of insects to test the classification performance. Experimental results show that the proposed approach outperforms other advanced CNN-based models. More importantly, our attention-selection mechanism demonstrates good robustness to augmented images.

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Classification and detection of insects from field images using deep learning for smart pest management: A systematic review

Cited by 115 publications

References 77 publications

Multiclass insect counting through deep learning-based density maps estimation

Multiclass insect counting through deep learning-based density maps estimation

An Efficient System for Real-time Mobile Smart Device-based Insect Detection

Insect recognition based on complementary features from multiple views

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