ILCAN: A New Vision Attention-Based Late Blight Disease Localization and Classification

Pattanaik, Priyadarshini Adyasha; Khan, Mohammad Zubair; Pattnaik, Prasant Kumar

doi:10.1007/s13369-021-06201-6

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Conspicuous peaks and valleys of attention curves might elucidate that during the learning progress of LPnet highlighted valuable wavelengths in the form of positive attention and negative attention. Comparison of the finding with those of other studies confirmed that the attention module inside the deep learning structure was able to emphasize informative features in relation to the assigned object [ 47 , 48 ]. A total of 4 distinct wavelengths and their learned weights were clearly noted, including 513 nm, 536 nm, 673 nm, and 679 nm.…”

Section: Resultssupporting

confidence: 72%

Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning

Zhang

Feng

et al. 2022

Plant Methods

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Background Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. Results In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS–NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. Conclusions This study illustrated that informative VIS–NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.

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Section: Resultssupporting

confidence: 72%

Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning

Zhang

Feng

et al. 2022

Plant Methods

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“…SVM were implemented for sugar beet disease ( Rumpf et al., 2010 ) and depending upon severity of disease, classification accuracy of 65% was achieved when 1-2% area of the leaf was diseased and accuracy increased to 90% when diseased area of the leaf was 10%-15%. Pattanaik A. P ( Pattanaik et al., 2022 ). proposed an approach where late blight disease was detected using “Improving Localization and Classification with Attention Consistent Network” (ILCAN) approach and achieved 98.9% accuracy which was better than the accuracy of 91.43% achieved by Grad-CAM++ ( Chattopadhay et al., 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Crop-saving with AI: latest trends in deep learning techniques for plant pathology

Salman,

Muhammad,

Piran

et al. 2023

Front. Plant Sci.

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Plant diseases pose a major threat to agricultural production and the food supply chain, as they expose plants to potentially disruptive pathogens that can affect the lives of those who are associated with it. Deep learning has been applied in a range of fields such as object detection, autonomous vehicles, fraud detection etc. Several researchers have tried to implement deep learning techniques in precision agriculture. However, there are pros and cons to the approaches they have opted for disease detection and identification. In this survey, we have made an attempt to capture the significant advancements in machine-learning based disease detection. We have discussed prevalent datasets and techniques that have been employed as well as highlighted emerging approaches being used for plant disease detection. By exploring these advancements, we aim to present a comprehensive overview of the prominent approaches in precision agriculture, along with their associated challenges and potential improvements. This paper delves into the challenges associated with the implementation and briefly discusses the future trends. Overall, this paper presents a bird’s eye view of plant disease datasets, deep learning techniques, their accuracies and the challenges associated with them. Our insights will serve as a valuable resource for researchers and practitioners in the field. We hope that this survey will inform and inspire future research efforts, ultimately leading to improved precision agriculture practices and enhanced crop health management.

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“…To acquire greater power and flexibility, Deep Learning (DL) draws inspiration from biological neural networks. In contrast to typical ML approaches [8], which are restricted in their ability to handle complicated problems like the classification of medical images, DL is inspired by biological neural networks to achieve better power and flexibility.…”

Section: Introductionmentioning

confidence: 99%

Deep Feature Detection Approach for COVID-19 Classification based on X-ray Images

Noor¹,

Pattanaik²,

Khan³

et al. 2023

IJACSA

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The novel human Corona disease (COVID-19) is a pulmonary sickness brought on by an extraordinarily outrageous respiratory condition crown 2. (SARS -CoV-2). Chest radiography imaging has a significant role in the screening, early diagnosis, and follow-up of the suspected individuals due to the effects of COVID-19 on pneumonic-sensitive tissue. It also has a severe impact on the economy as a whole. If positive patients are identified early, the spread of the pandemic illness can be slowed. To determine whether people are at risk for illnesses, a COVID-19 infection prediction is critical. This paper categorizes chest CT samples of COVID-19 affected patients. The two-stage proposed deep learning technique produces spatial function from images, so it is a very expeditious manner for image category hassle. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) image datasets. Comparative evaluation reveals that our proposed method outperforms amongst other 20 different existing pre-trained models. The test outcomes constitute that our proposed model achieved the best rating of 97.6%, 0.964, 0.964, and 0.982 concerning the accuracy, precision, recall, specificity, and F1score, respectively.

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ILCAN: A New Vision Attention-Based Late Blight Disease Localization and Classification

Cited by 5 publications

References 20 publications

Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning

Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning

Crop-saving with AI: latest trends in deep learning techniques for plant pathology

Deep Feature Detection Approach for COVID-19 Classification based on X-ray Images

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