The Identification of Powdery Mildew Spores Image Based on the Integration of Intelligent Spore Image Sequence Capture Device

Danping, Wang; Wang, Botao; Yue, Yan

doi:10.1109/iih-msp.2013.53

Cited by 10 publications

(13 citation statements)

References 1 publication

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“…In [31], an approach is introduced to perform the identification task of powdery mildew spores, which is essential to related research. The workflow of this approach is shown in Fig.…”

Section: Classification Tasksmentioning

confidence: 99%

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Zhang¹,

Li²,

Yin³

et al. 2021

Preprint

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Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

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“…In [31], an approach is introduced to perform the identification task of powdery mildew spores, which is essential to related research. The workflow of this approach is shown in Fig.…”

Section: Classification Tasksmentioning

confidence: 99%

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Zhang¹,

Li²,

Yin³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The fungi affects various plants as well, causing several forms of diseases, Table 4 summarises span of work over the years concentrating on detection of diseases caused by fungus. Various diseases which occur due to fungus are corynespora [15], bird's eye spot [15], powdery mildew [30, 47–50], downy mildew [20, 30, 51–53], scab [29],black spot [54], red spot [55], rust [20], anthracnose [56, 57], melanose [58], frogeye [59], curvularia leaf spot [60], wheat stripe rust [61], septoria leaf spot [62]. Dataset varies from 40 to 1500 images, with small dataset of 40 images Youwen et al [30] achieved 100% accuracy in detecting powdery mildew, and with 1478 images Meunkaewjinda et al [29] achieved an accuracy of 82.5 and 83.5% for detecting scab and rust diseases in grape.…”

Section: Categorical Classification Of Algorithmic Techniquesmentioning

confidence: 99%

Review of image processing approaches for detecting plant diseases

Sinha

Shekhawat

2020

IET Image Processing

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“…α t (y) = n b /n o if y = 1 1 otherwise (10) In the above equations, n b and n o are respectively the numbers of background pixel and object pixel, which means that the loss of small class will be amplified and the loss focus on the small class. It is noted that α t can be calculated from dataset.…”

Section: Constrained Focal Loss (Cfl)mentioning

confidence: 99%

“…In addition, Artificial Neural Network (ANN) is used for the detection of spores. For example, in [10], a semiautomated counting method of arbuscular mycorrhizal fungi spores is proposed. In [11], ANN is employed to automatically detect and count powdery mildew spores.…”

Section: Introductionmentioning

confidence: 99%

Constrained-Focal-Loss Based Deep Learning for Segmentation of Spores

et al. 2019

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The statistics of disease spores is significant for early strategy design of disease control in precision agriculture. To obtain the statistics information of spores in microscopic images, it is crucial to segment spores from images. In this paper, we research a deep learning based method to segment spores, taking anthrax spores as the research objects. We first built an anthrax spore dataset consisting of more than 40,000 spores with accurate labeled spore boundaries to advance the state of the art technology of spore statistics. Then on consideration of the complex class imbalances in actual anthrax spore images, we investigate how class imbalances and hard examples simultaneously influence the loss during training and we discover that hard examples are more likely to appear at the pixels of rare pixels, such as small class pixels and contour pixels. Based on this discovery, we propose Constrained Focal Loss (CFL), which focuses on small class objects, and has a constrained term related to hard examples. In addition, we further propose CFL * , where high importance is put on the pixels surrounding spore contours to improve classification accuracy. The results show that the mean IoU of the DeepLabv3+ trained with CFL * (called as CFL * Net) achieves 91.0%, higher than original DeepLabv3+ with cross-entropy by 8.6 points, and the DeepLabv3+ with Focal Loss by 10.4 points. Moreover, CFLNet * can achieve better performance than original DeepLabv3+, using less than one-third of the training samples and half of the training steps. INDEX TERMS Image segmentation, class imbalance, focal Loss, hard example, convolutional neural networks (CNN).

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The Identification of Powdery Mildew Spores Image Based on the Integration of Intelligent Spore Image Sequence Capture Device

Cited by 10 publications

References 1 publication

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Review of image processing approaches for detecting plant diseases

Constrained-Focal-Loss Based Deep Learning for Segmentation of Spores

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