Development of an automatic pest monitoring system using a deep learning model of DPeNet

Zhao, Nan; Zhou, Lei; Huang, Ting; Taha, Mohamed Farag; He, Yong; Qiu, Zhengjun

doi:10.1016/j.measurement.2022.111970

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…Expanding of image data frame could benefit to fitting the large model training requirements and achieve the desired insect identification results. In the DPeNet model-based automatic insect monitoring system developed by Zhao et al (2022), 325 original images were collected and expanded to 22,815 for model training by means of data enhancement. Compared with Muscidae (99.1%), Araneae (100%), Apis (100%) and other insects with larger target sizes (1.5-2 cm), the trained model in this study had the recognition rate of 97.7% for small target pests (1-3 mm).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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S-ResNet: An improved ResNet neural model capable of the identification of small insects

et al. 2022

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IntroductionPrecise identification of crop insects is a crucial aspect of intelligent plant protection. Recently, with the development of deep learning methods, the efficiency of insect recognition has been significantly improved. However, the recognition rate of existing models for small insect targets is still insufficient for insect early warning or precise variable pesticide application. Small insects occupy less pixel information on the image, making it more difficult for the model to extract feature information.MethodsTo improve the identification accuracy of small insect targets, in this paper, we proposed S-ResNet, a model improved from the ResNet, by varying its convolution kernel. The branch of the residual structure was added and the Feature Multiplexing Module (FMM) was illustrated. Therefore, the feature expression capacity of the model was improved using feature information of different scales. Meanwhile, the Adjacent Elimination Module (AEM) was furtherly employed to eliminate the useless information in the extracted features of the model.ResultsThe training and validation results showed that the improved residual structure improved the feature extraction ability of small insect targets compared to the original model. With compare of 18, 30, or 50 layers, the S-ResNet enhanced the identification accuracy of small insect targets by 7% than that on the ResNet model with same layer depth.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

S-ResNet: An improved ResNet neural model capable of the identification of small insects

et al. 2022

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“…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%

“…AI systems have demonstrated remarkable success in recognizing and identifying objects in images. By training AI to recognize the visual characteristics of different insect pests, such as their colour, shape, and size, these systems can be used to analyse images of crops and ecosystems and identify pests that are present (Azfar et al., 2023; Chithambarathanu & Jeyakumar, 2023; de Telmo & Rieder, 2020; Domingues et al., 2022; Li et al., 2021; Li, Wang, et al., 2020; Lima et al., 2020; Liu et al., 2019; Partel et al., 2019; Zhao, Liu, et al., 2022; Zhao, Zhou, et al., 2022). The training process for AI involves feeding them a large number of images of different pests and allowing them to learn the patterns and features that distinguish them from one another.…”

Section: Fields That Benefit From Ai Methodsmentioning

confidence: 99%

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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“…Current methods for depth localization using monocular cameras focus on both image feature matching as well as deep learning [1,2] . Huang et al used the camera projection principle to solve the target contour segmentation information and its pose angle and calculate the target distance based on the projected area [3] .…”

Section: Introductionmentioning

confidence: 99%

Monocular camera-based large field-of-view depth estimation for mobile machining of large aerospace components

Tian,

Cui,

Han

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

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In the integrated processing of large components in the aerospace field, for the end servo industrial robot positioning accuracy needs, often using binocular vision positioning method. This method can accurately measure the end position but is limited by the restricted field of view and small depth of field. So, a monocular camera is needed for servo-guiding the processing end of the robot arm. Therefore, a monocular depth estimation method based on improved Yolov8 and CNN fusion for mobile machining with large field-of-view is proposed in this paper. Firstly, a dataset construction method based on virtual-real fusion is proposed to solve the problem that the depth information corresponding to the training set images is difficult to measure; secondly, the proposed Yolov8sim-CNN cascade neural network can realize the measurement of fast localization and depth prediction of the target machining workpiece and realize the servo-guidance of the robot arm end. The experimental results show that the proposed Yolov8sim-CNN network can ensure high detection accuracy, and the detection accuracy is substantially improved compared with the method of CNN-only, which indicates that the proposed method has better fitting ability and higher accuracy.

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Development of an automatic pest monitoring system using a deep learning model of DPeNet

Cited by 14 publications

References 21 publications

S-ResNet: An improved ResNet neural model capable of the identification of small insects

S-ResNet: An improved ResNet neural model capable of the identification of small insects

Artificial neuronal networks are revolutionizing entomological research

Monocular camera-based large field-of-view depth estimation for mobile machining of large aerospace components

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