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McClellanville Library
Closed for renovations
Phone: (843) 887-3699
Miss Jane's Building (Edisto Library Temporary Location)
9 a.m. - 4 p.m.
Phone: (843) 869-2355
Main Library
9 a.m. - 8 p.m.
Phone: (843) 805-6930
West Ashley Library
9 a.m. - 7 p.m.
Phone: (843) 766-6635
Folly Beach Library
Closed for renovations
Phone: (843) 588-2001
John L. Dart Library
9 a.m. - 7 p.m.
Phone: (843) 722-7550
St. Paul's/Hollywood Library
9 a.m. - 8 p.m.
Phone: (843) 889-3300
Mt. Pleasant Library
9 a.m. – 8 p.m.
Phone: (843) 849-6161
Dorchester Road Library
9 a.m. - 8 p.m.
Phone: (843) 552-6466
Edgar Allan Poe/Sullivan's Island Library
9 p.m. - 6 p.m.
Phone: (843) 883-3914
John's Island Library
9 a.m. - 8 p.m.
Phone: (843) 559-1945
Wando Mount Pleasant Library
9 a.m. - 8 p.m.
Phone: (843) 805-6888
Otranto Road Library
9 a.m. - 8 p.m.
Phone: (843) 572-4094
Hurd/St. Andrews Library
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Baxter-Patrick James Island
9 a.m. - 8 p.m.
Phone: (843) 795-6679
Bees Ferry West Ashley Library
9 p.m. - 8 p.m.
Phone: (843) 805-6892
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
Keith Summey North Charleston Library
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Phone: (843) 744-2489
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Phone: (843) 805-6909
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Potato disease prediction using machine learning, image processing and IoT – a systematic literature survey.
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- Author(s): Gupta, Ayushi; Chug, Anuradha; Singh, Amit Prakash
- Source:
Journal of Crop Improvement; Mar2024, Vol. 38 Issue 2, p95-137, 43p- Subject Terms:
- Source:
- Additional Information
- Abstract: Potato is one of the most nutritious foods rich in fiber and essential vitamins and minerals. However, potato plants may suffer from diseases such as early blight, late blight, septoria leaf spot or skin diseases such as greening, silver scurf, common scab, black rot etc. These diseases may lead to huge losses in the potato yield annually. Hence, an early prediction of such diseases is required so that they can be controlled at an early stage. Several machine learning, IoT based and image processing techniques have been utilized for this purpose, and this paper aims to survey all such methods and prediction models. A total of 116 studies were analyzed, and out of them, 77 relevant studies belonging to (60%) journals, (34%) conferences and (6%) other sources have been surveyed. It has been observed that most of the selected studies (64%) have used RGB image data (proprietary or PlantVillage dataset). Also, majority of the studies (48%) have worked with imbalanced data. Late blight and early blight are the two diseases that have gained the most attention by the researchers, being included in 58% and 34% of the studies. Moreover, feature extraction/selection has been used only with image data (RGB and hyperspectral). Convolutional Neural Network is the most widely used classifier, followed by Support Vector Machines. This review will help the researchers gain an insight into the work done for disease detection in potato crop and what techniques or diseases need intervention so that yield losses can further be minimized. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Journal of Crop Improvement is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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