Improved CNN Method for Crop Pest Identification Based on Transfer Learning

Liu, Yiwen; Zhang, Xian; Gao, Yanxia; Qu, Taiguo; Shi, Yuanquan

doi:10.1155/2022/9709648

Cited by 27 publications

(12 citation statements)

References 25 publications

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“…Several studies have demonstrated the effectiveness of AI‐based insect identification in the field. For example, one study found that a CNN achieved an accuracy rate of 97% in identifying wild bee species (Buschbacher, Buschbacher, et al., 2020) and various pest species with similar precision (Johari et al., 2023; Liu et al., 2022). Another study used an AI algorithm to classify ladybird beetles with an accuracy rate of 92% (Venegas et al., 2021).…”

Section: Ai Methods For Species Identificationmentioning

confidence: 99%

“…In several studies, AI systems have been used to identify and track the spread of insect pests that can cause significant damage to crops and ecosystems (Aigner et al., 2016; Caselli & Petacchi, 2021; Chithambarathanu & Jeyakumar, 2023; Deka et al., 2022; He et al., 2019; Li et al., 2021; Liu et al., 2022; Xia et al., 2018; Zhao, Liu, et al., 2022; Zhao, Zhou, et al., 2022). For example, researchers have used AI to analyse satellite imagery to identify areas where pest outbreaks are occurring, providing an early warning and allowing for proactive management strategies (Gómez‐Camperos et al., 2022; Meraj et al., 2022; Pourghasemi, 2021).…”

Section: Fields That Benefit From Ai Methodsmentioning

confidence: 99%

“…Several studies have demonstrated the effectiveness of AIbased insect identification in the field. For example, one study found that a CNN achieved an accuracy rate of 97% in identifying wild bee species and various pest species with similar precision (Johari et al, 2023;Liu et al, 2022).…”

Section: Obtaining a Comprehensive And Diverse Dataset For Trainingmentioning

confidence: 99%

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Artificial neuronal networks are revolutionizing entomological research

Hartbauer

2024

J Applied Entomology

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The application of artificial intelligence (AI) in entomological research has gained significant attention in recent years. This review summarizes the current state of research on the potential of AI methods in various subfields of entomology, such as behavioural biology, biodiversity research, climate change research, pest management, and disease vector control. In some cases, AI‐based species identification methods based on deep learning neuronal network models have been shown to outperform traditional morphological identification methods in terms of accuracy and speed. Behavioural biology research has been enhanced through the use of AI‐based tracking systems that can classify insect behaviour and movement patterns. Habitat modelling has also been improved with the use of AI, allowing for the creation of more accurate models that can predict insect distribution and abundance. Climate change and biodiversity research have benefited from AI‐driven tools that can analyse large datasets and predict the impact of environmental changes on insect populations. In agriculture, pest management has been revolutionized by AI methods, with the development of smart traps and monitoring systems that can detect and identify pest species in real‐time, enabling targeted control measures. Disease vector control has also been improved through the use of AI‐based predictive models, which can identify areas at risk of disease transmission and aid in the development of effective control strategies. In conclusion, AI methods have the potential to revolutionize many aspects of entomological research. Future research should focus on developing more sophisticated AI tools and integrating them into entomological research to address even more complex ecological questions.

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Section: Ai Methods For Species Identificationmentioning

confidence: 99%

Section: Fields That Benefit From Ai Methodsmentioning

confidence: 99%

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Artificial neuronal networks are revolutionizing entomological research

Hartbauer

2024

J Applied Entomology

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“…Bao et al [26] developed a new technique e-ISSN : 0976-5166 p-ISSN : 2231-3850 for diagnosing wheat leaf diseases and severity based on elliptical-maximum margin criteria metric learning; it has an identification accuracy of 94.16 percent. Based on Inception-ResNet-v2 and VGG-16, Liu et al [29] developed a system for identifying and categorising crop pests. On the IDADP dataset, it earned an average accuracy score of 97.71%.…”

Section: Literature Surveymentioning

confidence: 99%

The Novel ML Approaches on Wheat Disease by CLF and FOHF of Image Equalization Techniques

Kanna.R¹,

V.²

2023

INDJCSE

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Crop diseases and insect pests are the most significant obstacles to agricultural production. Consequences include a decrease in crop yields and the obstruction of the long-term development of high-quality, high-efficiency agriculture. Rapid and accurate detection of wheat leaf disease categories promotes early deployment of field control and improvement of wheat yield and quality. It's no exaggeration to say that wheat is one of the world's most vital crops. Wheat leaf diseases, though, severely hamper development. The quality of wheat and the agricultural economy rely on prompt and correct identification of wheat leaf diseases. This work propose an integrated machine learning technique, According to the results of this study, when compared to other models, the Fuzzy Opponent Histogram Filter that makes use of Bayes Net performs the best. The Fuzzy Opponent Histogram Filter with Bayes Net gives best outcome which is 97% of accuracy.

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“…The former includes YOLO [1], SSD [2], and so on; the detection results are available immediately after testing, so it has a faster detection speed but is less capable of detecting small target spodoptera frugiperda. Faster-CNN [3], Mask R-CNN [4], and others are examples of the latter, which have slightly higher recognition accuracy [5], but slightly lower detection speed and cannot meet real-time performance requirements. Both methods have advantages, but neither can simultaneously meet the accuracy and real-time requirements of spodoptera frugiperda detection [6,7].…”

Section: Introductionmentioning

confidence: 99%

An Spodoptera Frugiperda pest detection algorithm based on improved feature fusion

Chen

Guan

2023

International Conference on Electronic Information Engineering and Computer Science (EIECS 2022)

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Maize is one of the important foods and feed crops with high agricultural value in the agricultural fields of northern China. Accurate detection and treatment of nightshade spodoptera frugiperda in agricultural fields is essential to increase agricultural production. Compared with the traditional manual detection methods, the convolutional neural network-based spodoptera frugiperda detection algorithm has the better recognition performance. However, the method of using convolutional neural networks for the recognition of spodoptera frugiperda still suffers from low accuracy and slow detection speed. To improve the detection accuracy and speed of the spodoptera frugiperda, the algorithm modified the information fusion based on the YOLOv3 model, and used quadruple upsampling fusion instead of the original double upsampling fusion to remove the redundant information brought by repeated fusion. In the experiment, the dataset consists of 4000 training images and 200 test images for training and testing. The experimental data show that the improved YOLOv3 algorithm improves the accuracy by 11.42% and the detection time by 0.0023s compared with that before the improvement.

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Improved CNN Method for Crop Pest Identification Based on Transfer Learning

Cited by 27 publications

References 25 publications

Artificial neuronal networks are revolutionizing entomological research

Artificial neuronal networks are revolutionizing entomological research

The Novel ML Approaches on Wheat Disease by CLF and FOHF of Image Equalization Techniques

An Spodoptera Frugiperda pest detection algorithm based on improved feature fusion

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