Vegetable Pest Image Recognition Method Based on Improved VGG Convolution Neural Network

Ye, Haijian; Han, Hang; Zhu, Linna; Duan, Qingling

doi:10.1088/1742-6596/1237/3/032018

Cited by 11 publications

(3 citation statements)

References 3 publications

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“…We would like to use CNN only with pre-trained model to show the performance in accuracy and processing time that we will get. We used VGG or Visual Geometry Group pre-trained model that have 19 deep layers, or VGG19 [16]. This architecture shows that a deep layer in CNN is an important factor to create a classification system that have high result accuracy.…”

Section: Methodsmentioning

confidence: 99%

Handwriting identification using deep convolutional neural network method

2020

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Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.

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Section: Methodsmentioning

confidence: 99%

Handwriting identification using deep convolutional neural network method

2020

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“…Weed identification using deep learning (Jin et al, 2012;Xu et al, 2021) can not only improve the weed identification rate and rationalize the use of weed control methods; but also make efficient use of herbicides to protect the environment. As innovations in deep learning theory and hardware conditions continue to develop, people can construct deeper network models to extract more features (Ye et al, 2019;Wagle and Harikrishnan, 2021), and an increasing number of network models are being constructed for use in various aspects of agricultural production (Chakraborty et al, 2021). Deep learning has been widely applied in recent years, especially in smart agriculture fields, such as pest and disease detection (Mique and Palaoag, 2018;Liu et al, 2022;Wu et al, 2022), plant and fruit recognition (Jaiganesh et al, 2020;Bongulwar Deepali, 2021), and crop and weed detection and classification (Pando et al, 2018;Jin et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A new model based on improved VGG16 for corn weed identification

Yang

et al. 2023

Front. Plant Sci.

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Weeds remain one of the most important factors affecting the yield and quality of corn in modern agricultural production. To use deep convolutional neural networks to accurately, efficiently, and losslessly identify weeds in corn fields, a new corn weed identification model, SE-VGG16, is proposed. The SE-VGG16 model uses VGG16 as the basis and adds the SE attention mechanism to realize that the network automatically focuses on useful parts and allocates limited information processing resources to important parts. Then the 3 × 3 convolutional kernels in the first block are reduced to 1 × 1 convolutional kernels, and the ReLU activation function is replaced by Leaky ReLU to perform feature extraction while dimensionality reduction. Finally, it is replaced by a global average pooling layer for the fully connected layer of VGG16, and the output is performed by softmax. The experimental results verify that the SE-VGG16 model classifies corn weeds superiorly to other classical and advanced multiscale models with an average accuracy of 99.67%, which is more than the 97.75% of the original VGG16 model. Based on the three evaluation indices of precision rate, recall rate, and F1, it was concluded that SE-VGG16 has good robustness, high stability, and a high recognition rate, and the network model can be used to accurately identify weeds in corn fields, which can provide an effective solution for weed control in corn fields in practical applications.

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“…Midtvedt, et al [32] developed a weighted average convolutional network to analyze the hologram of single suspended nanoparticle and quantified the size and refractive index of a single subwavelength particle. But until now, these works mainly focus on relatively large [33,34], spherical object [35,36], sparse small particle field [37][38][39] or other objects (e.g., fiber internal structure [40], cell identification [41]), yet few focused on dense particle field consisted of liquid droplets and filaments with various morphological shapes like gel atomization field. And the combination of digital holography and deep learning methods were also extended to other particle-like objects, Belashov, et al [42] utilized holographic microscopy combined with cell segmentation algorithm using machine learning to characterize the dynamic process of apoptosis and the accuracy achieved 95.5% and Wang, et al [43] segmented some terahertz images of gear wheel and used average structural similarity to get the relatively best results which were proved to be better than some traditional segmentation algorithms in their paper.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Multiscale Dense Gel Filament-Droplet Field in Digital Holography With Mo-U-Net

et al. 2021

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Accurate particle detection is a common challenge in particle field characterization with digital holography, especially for gel secondary breakup with dense complex particles and filaments of multi-scale and strong background noises. This study proposes a deep learning method called Mo-U-net which is adapted from the combination of U-net and Mobilenetv2, and demostrates its application to segment the dense filament-droplet field of gel drop. Specially, a pruning method is applied on the Mo-U-net, which cuts off about two-thirds of its deep layers to save its training time while remaining a high segmentation accuracy. The performances of the segmentation are quantitatively evaluated by three indices, the positive intersection over union (PIOU), the average square symmetric boundary distance (ASBD) and the diameter-based prediction statistics (DBPS). The experimental results show that the area prediction accuracy (PIOU) of Mo-U-net reaches 83.3%, which is about 5% higher than that of adaptive-threshold method (ATM). The boundary prediction error (ASBD) of Mo-U-net is only about one pixel-wise length, which is one third of that of ATM. And Mo-U-net also shares a coherent size distribution (DBPS) prediction of droplet diameters with the reality. These results demonstrate the high accuracy of Mo-U-net in dense filament-droplet field recognition and its capability of providing accurate statistical data in a variety of holographic particle diagnostics. Public model address: https://github.com/Wu-Tong-Hearted/Recognition-of-multiscale-dense-gel-filament-droplet-field-in-digital-holography-with-Mo-U-net.

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Vegetable Pest Image Recognition Method Based on Improved VGG Convolution Neural Network

Cited by 11 publications

References 3 publications

Handwriting identification using deep convolutional neural network method

Handwriting identification using deep convolutional neural network method

A new model based on improved VGG16 for corn weed identification

Recognition of Multiscale Dense Gel Filament-Droplet Field in Digital Holography With Mo-U-Net

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