Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM

Huang, Shuangjie; Zhou, Guofu; He, Mingyi; Chen, Aibin; Hu, Yahui

doi:10.1109/access.2020.3011685

Cited by 31 publications

(10 citation statements)

References 38 publications

(41 reference statements)

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“…In recent research works, the deep neural network based models have been developed and it mainly uses convolutional neural network for plant disease detection. A peach disease detection method 19 is based on ANLM (asymptotic nonlocal means image algorithm) to reduce the intervention of the background in the input image and the fusion of PCNN (parallel convolution neural network) and ELM (extreme learning machine) optimized by linear particle swarm optimization (IPSO) for recognize the peach disease characteristics to improve network accuracy. They used 25,513 input images to predict the peach diseases such as brown rot, anthracnose, black spot, and scab effectively.…”

Section: Related Workmentioning

confidence: 99%

“…In recent research works, the deep neural network based models have been developed and it mainly uses convolutional neural network for plant disease detection. A peach disease detection method 19 To attain maximum accuracy, 24 presented four deep neural network models for predicting the diseases on soybean leaves using UAV input images and the models can be trained with various parameters for transfer learning and especially for fine tuning. This model achieves a high classification rate with accuracy of 99.04%.…”

Section: Related Workmentioning

confidence: 99%

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Integration of dilated convolution with residual dense block network and multi‐level feature detection network for cassava plant leaf disease identification

Kandasamy¹,

Rajendran

2022

Concurrency and Computation

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In recent years, many countries prolonged their research in the field of agricultural farming for yielding more quantity food products with good quality. Agricultural farming is closely related to every country's economy in significant way. To improve the grain productivity in a notable way, plants must be protected from various viral, fungal, and bacterial diseases. The timely identification of plant disease helps to protect the plant at the early stage of the disease else it will spoil the entire plant. In this article, the plant disease identification problem is handled using our novel proposed network where it combines dilated convolution with residual dense block (DCRDB) along with multi-level feature detection (MLFD) for selecting the appropriate feature and bidirectional long short-term memory (Bi-LSTM) classifier for leaf disease prediction. In DCRDB, the dilated convolution derives larger receptive field without loss of resolution to extract greater number of local features from the leaf images and residual dense block expands the size of feature map by densely connecting the layers of convolution. In addition, MLFD network integrates the high-level features with the resulted features of DCRDB. After feature extraction, Bi-LSTM is employed to recognize the leaf disease which supports additional training in both forward and backward direction. Through a set of collected cassava leaf images our research carries forward. The proposed model accuracy is verified with three different datasets such as cassava disease classification dataset, self-made cassava disease dataset and plant village dataset.The performance of the proposed model is compared with some existing methods.Experimental results show that the highest F1-score value of 95.49% attained by our proposed model for identifying cassava leaf disease. K E Y W O R D Sagriculture, cassava leaf disease, dilated convolution, leaf disease identification, residual dense block, multilevel feature detection, plant disease INTRODUCTIONIn the field of agriculture, crop productivity is a major key challenge in many countries. Hence, it is necessary to increase the crop productivity that saves the people's lives in poverty and supports the wealth of the country. One of the major ways to increase the yield is to protect the crops from various viral, fungal, and bacterial diseases. 1 At the initial stage of the disease, the leaves of the plant are first affected, and then it spoils the entire

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Section: Related Workmentioning

confidence: 99%

“…In recent research works, the deep neural network based models have been developed and it mainly uses convolutional neural network for plant disease detection. A peach disease detection method 19 To attain maximum accuracy, 24 presented four deep neural network models for predicting the diseases on soybean leaves using UAV input images and the models can be trained with various parameters for transfer learning and especially for fine tuning. This model achieves a high classification rate with accuracy of 99.04%.…”

Section: Related Workmentioning

confidence: 99%

Integration of dilated convolution with residual dense block network and multi‐level feature detection network for cassava plant leaf disease identification

Kandasamy¹,

Rajendran

2022

Concurrency and Computation

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“…Disease-based leaf recognition methods are a popular research direction in computer vision and image processing [3][4][5]. Numerous studies have successfully combined image processing and traditional machine learning techniques, resulting in significant application value [6,7].…”

Section: Introductionmentioning

confidence: 99%

DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification

Liu

Cai

et al. 2022

Computational Intelligence and Neuroscience

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Tomato is an important and fragile crop. During the course of its development, it is frequently contaminated with bacteria or viruses. Tomato leaf diseases may be detected quickly and accurately, resulting in increased productivity and quality. Because of the intricate development environment of tomatoes and their inconspicuous disease spot features and small spot area, present machine vision approaches fail to reliably recognize tomato leaves. As a result, this research proposes a novel paradigm for detecting tomato leaf disease. The INLM (integration nonlocal means) filtering algorithm, for example, decreases the interference of surrounding noise on the features. Then, utilizing ResNeXt50 as the backbone, we create DCCAM-MRNet, a novel tomato image recognition network. Dilated Convolution (DC) was employed in STAGE 1 of the DCCAM-MRNet to extend the network's perceptual area and locate the scattered disease spots on tomato leaves. The coordinate attention (CA) mechanism is then introduced to record cross-channel information and direction- and position-sensitive data, allowing the network to more accurately detect localized tomato disease spots. Finally, we offer a mixed residual connection (MRC) technique that combines residual block (RS-Block) and transformed residual block (TR-Block) (TRS-Block). This strategy can increase the network's accuracy while also reducing its size. The DCCAM-classification MRNet's accuracy is 94.3 percent, which is higher than the existing network, and the number of parameters is 0.11 M lesser than the backbone network ResNeXt50, according to the experimental results. As a result, combining INLM and DCCAM-MRNet to identify tomato diseases is a successful strategy.

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“…In addition, conventional image processing methods struggle to extract deeper feature information and often are not readily applicable to real environment settings. Recently, Huang et al (2020) proposed PCNN-IPELM to detect peach diseases, and its detection effect is considered good. However, convolution only uses local information to calculate the target pixel, possibly leading to a loss of information given the lack of global features.…”

Section: Introductionmentioning

confidence: 99%

MDAM-DRNet: Dual Channel Residual Network With Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection

et al. 2022

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The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases' spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops. To that end, here we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet). (1) In order to fully extract the color features from images of diseased strawberry leaves, this paper constructed a color feature path at the front end of the network. The color feature information in the image was then extracted mainly through a color correlogram. (2) Likewise, to fully extract the texture features from images, a texture feature path at the front end of the network was built; it mainly extracts texture feature information by using an area compensation rotation invariant local binary pattern (ACRI-LBP). (3) To enhance the model's ability to extract detailed features, for the main frame, this paper proposed a multidirectional attention mechanism (MDAM). This MDAM can allocate weights in the horizontal, vertical, and diagonal directions, thereby reducing the loss of feature information. Finally, in order to solve the problems of gradient disappearance in the network, the ELU activation function was used in the main frame. Experiments were then carried out using a database we compiled. According to the results, the highest recognition accuracy by the network used in this paper for six types of strawberry leaf diseases and normal leaves is 95.79%, with an F1 score of 95.77%. This proves the introduced method is effective at detecting strawberry leaf diseases.

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Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM

Cited by 31 publications

References 38 publications

Integration of dilated convolution with residual dense block network and multi‐level feature detection network for cassava plant leaf disease identification

Integration of dilated convolution with residual dense block network and multi‐level feature detection network for cassava plant leaf disease identification

DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification

MDAM-DRNet: Dual Channel Residual Network With Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection

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