An ensemble learning integration of multiple <scp>CNN</scp> with improved vision transformer models for pest classification

Xia, Wanshang; Han, Dong‐Guk; Li, Dun; Wu, Zhongdai; Han, Bing; Wang, Junxiang

doi:10.1111/aab.12804

Cited by 17 publications

(4 citation statements)

References 32 publications

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“…When used in intricate settings to spot numerous plant diseases, the model yields effective and efficient outcomes. For detecting pests, several researchers have investigated object identification methods based on DL [45,46]. But none of this research covered the topic of identifying scale insect to preserve beneficial insects.…”

Section: Literature Reviewmentioning

confidence: 99%

Faster-PestNet: A Lightweight Deep Learning Framework for Crop Pest Detection and Classification

Ali,

Qayyum,

Iqbal

2023

IEEE Access

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One of the most significant risks impacting crops is pests, which substantially decrease food production. Further, prompt and precise recognition of pests can help harvesters save damage and enhance the quality of crops by enabling them to take appropriate preventive action. The apparent resemblance between numerous kinds of pests makes examination laborious and takes time. The limitations of physical pest inspection are required to be addressed, and a novel deep-learning approach called the Faster-PestNet is proposed in this work. Descriptively, an improved Faster-RCNN approach is designed using the MobileNet as its base network and tuned on the pest samples to recognize the crop pests of various categories and given the name of Fatser-PestNet. Initially, the MobileNet is employed for extracting a distinctive set of sample attributes, later recognized by the 2-step locator of the improved Faster-RCNN model. We have accomplished a huge experimentation analysis over a complicated data sample named the IP102 and acquired an accuracy of 82.43%. Further, a local crops dataset is also collected and tested on the trained Faster-PestNet approach to show the generalization capacity of the proposed model. We have confirmed through analysis that the presented work can tackle numerous sample distortions like noise, blurring, light variations, and size alterations and can accurately locate the pest along with the associated class label on the leaf of numerous types and sizes. Both visual and stated performance values confirm the effectiveness of our model.

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Section: Literature Reviewmentioning

confidence: 99%

Faster-PestNet: A Lightweight Deep Learning Framework for Crop Pest Detection and Classification

Ali,

Qayyum,

Iqbal

2023

IEEE Access

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“…The main advantage of ensemble learning is that by combining multiple base models, it can achieve better predictive performance than any single model, making it a simple and easy-to-implement model optimization strategy. The voting mechanism of ensemble learning can be roughly divided into two types: hard voting and soft voting [39,40]. Hard voting is to conduct majority voting on the category prediction values of multiple base models to obtain the final category prediction.…”

Section: Ensemble Learningmentioning

confidence: 99%

GA-based weighted ensemble learning for multi-label aerial image classification using convolutional neural networks and vision transformers

Tseng

2023

Mach. Learn.: Sci. Technol.

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Multi-label classification (MLC) of aerial images is a crucial task in remote sensing image analysis. Traditional image classification methods have limitations in image feature extraction, leading to an increasing use of deep learning models, such as convolutional neural networks (CNN) and vision transformers (ViT). However, the standalone use of these models may have limitations when dealing with MLC. To enhance the generalization performance of MLC of aerial images, this paper combines two CNN and two ViT models, comparing four single deep learning models, a manually weighted ensemble learning method, and a GA-based weighted ensemble method. The experimental results using two public multi-label aerial image datasets show that the classification performance of ViT models is better than CNN models, the traditional weighted ensemble learning model performs better than a single deep learning model, and the GA-based weighted ensemble method performs better than the manually weighted ensemble learning method. The GA-based weighted ensemble method proposed in this study can achieve better MLC performance of aerial images than previous results.

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“…The potential of ViT models in pest classification have been evaluated by Xia et al (2022). The authors use ResNet50, MMAINet, DNVT and an ensemble learner combining the predictions of these three models in a final classification vote.…”

Section: Overview: Image Classification In Entomologymentioning

confidence: 99%

From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring

Batz¹,

Will

Thiel³

et al. 2023

Front. Plant Sci.

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Insect monitoring has gained global public attention in recent years in the context of insect decline and biodiversity loss. Monitoring methods that can collect samples over a long period of time and independently of human influences are of particular importance. While these passive collection methods, e.g. suction traps, provide standardized and comparable data sets, the time required to analyze the large number of samples and trapped specimens is high. Another challenge is the necessary high level of taxonomic expertise required for accurate specimen processing. These factors create a bottleneck in specimen processing. In this context, machine learning, image recognition and artificial intelligence have emerged as promising tools to address the shortcomings of manual identification and quantification in the analysis of such trap catches. Aphids are important agricultural pests that pose a significant risk to several important crops and cause high economic losses through feeding damage and transmission of plant viruses. It has been shown that long-term monitoring of migrating aphids using suction traps can be used to make, adjust and improve predictions of their abundance so that the risk of plant viruses spreading through aphids can be more accurately predicted. With the increasing demand for alternatives to conventional pesticide use in crop protection, the need for predictive models is growing, e.g. as a basis for resistance development and as a measure for resistance management. In this context, advancing climate change has a strong influence on the total abundance of migrating aphids as well as on the peak occurrences of aphids within a year. Using aphids as a model organism, we demonstrate the possibilities of systematic monitoring of insect pests and the potential of future technical developments in the subsequent automated identification of individuals through to the use of case data for intelligent forecasting models. Using aphids as an example, we show the potential for systematic monitoring of insect pests through technical developments in the automated identification of individuals from static images (i.e. advances in image recognition software). We discuss the potential applications with regard to the automatic processing of insect case data and the development of intelligent prediction models.

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An ensemble learning integration of multiple CNN with improved vision transformer models for pest classification

Cited by 17 publications

References 32 publications

Faster-PestNet: A Lightweight Deep Learning Framework for Crop Pest Detection and Classification

Faster-PestNet: A Lightweight Deep Learning Framework for Crop Pest Detection and Classification

GA-based weighted ensemble learning for multi-label aerial image classification using convolutional neural networks and vision transformers

From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring

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