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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Plant Disease Recognition and Detection - A Large Scale Benchmark Dataset and A Visual Region and Loss Reweighting Approach |
| Authors Name: | A Vivek , P Navaneetha Krishnan , P Arivazhagan |
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IJRTI_183963
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| Published Paper Id: | IJRTI2209006 |
| Published In: | Volume 7 Issue 9, September-2022 |
| DOI: | |
| Abstract: | Plant disease diagnosis is very critical for agricul- ture due to its importance for increasing crop production. Recent advances in image processing offer us a new way to solve this issue via visual plant disease analysis. However, there are few works in this area, not to mention systematic researches. In this paper, we systematically investigate the problem of visual plant disease recognition for plant disease diagnosis. Compared with other types of images, plant disease images generally exhibit randomly distributed lesions, diverse symptoms and complex backgrounds, and thus are hard to capture discriminative information. To facil- itate the plant disease recognition research, we construct a new large-scale plant disease dataset with 271 plant disease categories and 220,592 images. Based on this dataset, we tackle plant disease recognition via reweighting both visual regions and loss to emphasize diseased parts. We first compute the weights of all the divided patches from each image based on the cluster distribution of these patches to indicate the discriminative level of each patch. Then we allocate the weight to each loss for each patch-label pair during weakly-supervised training to enable discriminative disease part learning. We finally extract patch features from the network trained with loss reweighting, and utilize the LSTM network to encode the weighed patch feature sequence into a comprehensive feature representation. Extensive evaluations on this dataset and another public dataset demonstrate the advantage of the proposed method. We expect this research will further the agenda of plant disease recognition in the community of image processing. |
| Keywords: | Fine-grained visual classification, reweighting approach, feature aggregation, Plant disease recognition. |
| Cite Article: | "Plant Disease Recognition and Detection - A Large Scale Benchmark Dataset and A Visual Region and Loss Reweighting Approach", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 9, page no.45 - 59, September-2022, Available :http://www.ijrti.org/papers/IJRTI2209006.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2209006
Registration ID:183963
Published In: Volume 7 Issue 9, September-2022
DOI (Digital Object Identifier):
Page No: 45 - 59 Country: Thiruvarur, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2209006 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2209006 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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