EM-ERNet for image-based banana disease recognition

Lin, Haiqing; Zhou, Guoxiong; Chen, Aibin; Li, Jiayong; Li, Mingxuan; Hu, Yahui; Yu, Wen

doi:10.1007/s11694-021-01043-0

Cited by 14 publications

(4 citation statements)

References 22 publications

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“…Artificial intelligence has recently witnessed a booming development with a decent disease recognition performance as intelligent machines deployed in farms can reduce workload. Deep learning, a core technique of artificial intelligence, has been successfully adopted to recognize diseases or abnormalities, such as tomato (Fuentes et al, 2017;Liu and Wang, 2020;Wang et al, 2021), banana (Lin et al, 2021), potato (Gao et al, 2021), corn and apple (Zhong and Zhao, 2020), and many other plants (Gao et al, 2020;Liu and Wang, 2021). Recent studies (Martineau et al, 2017;Liu and Wang, 2021;Saranya et al, 2021) show the advantages and potentialities of deep learning methods compared to other methods, such as handcraft feature, in recognizing plant diseases and related tasks.…”

Section: Introductionmentioning

confidence: 99%

Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition

Yoon

Fuentes

et al. 2022

Front. Plant Sci.

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Deep learning shows its advantages and potentials in plant disease recognition and has witnessed a profound development in recent years. To obtain a competing performance with a deep learning algorithm, enough amount of annotated data is requested but in the natural world, scarce or imbalanced data are common, and annotated data is expensive or hard to collect. Data augmentation, aiming to create variations for training data, has shown its power for this issue. But there are still two challenges: creating more desirable variations for scarce and imbalanced data, and designing a data augmentation to ease object detection and instance segmentation. First, current algorithms made variations only inside one specific class, but more desirable variations can further promote performance. To address this issue, we propose a novel data augmentation paradigm that can adapt variations from one class to another. In the novel paradigm, an image in the source domain is translated into the target domain, while the variations unrelated to the domain are maintained. For example, an image with a healthy tomato leaf is translated into a powdery mildew image but the variations of the healthy leaf are maintained and transferred into the powdery mildew class, such as types of tomato leaf, sizes, and viewpoints. Second, current data augmentation is suitable to promote the image classification model but may not be appropriate to alleviate object detection and instance segmentation model, mainly because the necessary annotations can not be obtained. In this study, we leverage a prior mask as input to tell the area we are interested in and reuse the original annotations. In this way, our proposed algorithm can be utilized to do the three tasks simultaneously. Further, We collect 1,258 images of tomato leaves with 1,429 instance segmentation annotations as there is more than one instance in one single image, including five diseases and healthy leaves. Extensive experimental results on the collected images validate that our new data augmentation algorithm makes useful variations and contributes to improving performance for diverse deep learning-based methods.

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Section: Introductionmentioning

confidence: 99%

Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition

Yoon

Fuentes

et al. 2022

Front. Plant Sci.

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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 2012, the research group of Hinton (Krizhevsky et al, 2012) proposed to use the deep convolutional neural network AlexNet for image recognition on the ImageNet dataset, which greatly reduced the classification error rate and set off a wave of deep learning. In recent years, deep learning has shown certain advantages in image recognition and classification, object detection, and other fields (Lin et al, 2021). In the study of citrus disease identification, deep learning can achieve high recognition accuracy (Pan et al, 2019); this is because the convolutional neural network (CNN) feature extraction layer can automatically learn features from citrus samples and extract useful feature information, but the acquisition of citrus disease images under different environmental conditions and the use of different network models for recognition will bring different classification results.…”

Section: Introductionmentioning

confidence: 99%

DS-MENet for the classification of citrus disease

Liu

Zhou

et al. 2022

Front. Plant Sci.

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Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life.

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EM-ERNet for image-based banana disease recognition

Cited by 14 publications

References 22 publications

Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition

Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition

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

DS-MENet for the classification of citrus disease

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