Comprehensive watermelon disease recognition dataset
Plant diseases pose a significant obstacle to global agricultural productivity, impacting crop quality yield and causing substantial economic losses for farmers. Watermelon, a commonly cultivated succulent vine plant, is rich in hydration and essential nutrients. However, it is susceptible to variou...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-04-01
|
| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924001537 |
| _version_ | 1797256365722304512 |
|---|---|
| author | Mohammad Imtiaz Nakib M.F. Mridha |
| author_facet | Mohammad Imtiaz Nakib M.F. Mridha |
| author_sort | Mohammad Imtiaz Nakib |
| collection | DOAJ |
| description | Plant diseases pose a significant obstacle to global agricultural productivity, impacting crop quality yield and causing substantial economic losses for farmers. Watermelon, a commonly cultivated succulent vine plant, is rich in hydration and essential nutrients. However, it is susceptible to various diseases due to unfavorable environmental conditions and external factors, leading to compromised quality and substantial financial setbacks. Swift identification and management of crop diseases are imperative to minimize losses, enhance yield, reduce costs, and bolster agricultural output. Conventional disease diagnosis methods are often labor-intensive, time-consuming, ineffective, and prone to subjectivity. As a result, there is a critical need to advance research into machine-based models for disease detection in watermelons. This paper presents a large dataset of watermelons that can be used to train a machine vision-based illness detection model. Images of healthy and diseased watermelons from the Mosaic Virus, Anthracnose, and Downy Mildew Disease are included in the dataset's five separate classifications. Images were painstakingly collected on June 25, 2023, in close cooperation with agricultural experts from the highly regarded Regional Horticulture Research Station in Lebukhali, Patuakhali. |
| first_indexed | 2024-03-07T21:53:35Z |
| format | Article |
| id | doaj.art-b517011dcace4c6591cb8516a1b66408 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-24T22:20:35Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-b517011dcace4c6591cb8516a1b664082024-03-20T06:10:02ZengElsevierData in Brief2352-34092024-04-0153110182Comprehensive watermelon disease recognition datasetMohammad Imtiaz Nakib0M.F. Mridha1Department of Computer Science, American International University-Bangladesh, Dhaka, BangladeshCorresponding author.; Department of Computer Science, American International University-Bangladesh, Dhaka, BangladeshPlant diseases pose a significant obstacle to global agricultural productivity, impacting crop quality yield and causing substantial economic losses for farmers. Watermelon, a commonly cultivated succulent vine plant, is rich in hydration and essential nutrients. However, it is susceptible to various diseases due to unfavorable environmental conditions and external factors, leading to compromised quality and substantial financial setbacks. Swift identification and management of crop diseases are imperative to minimize losses, enhance yield, reduce costs, and bolster agricultural output. Conventional disease diagnosis methods are often labor-intensive, time-consuming, ineffective, and prone to subjectivity. As a result, there is a critical need to advance research into machine-based models for disease detection in watermelons. This paper presents a large dataset of watermelons that can be used to train a machine vision-based illness detection model. Images of healthy and diseased watermelons from the Mosaic Virus, Anthracnose, and Downy Mildew Disease are included in the dataset's five separate classifications. Images were painstakingly collected on June 25, 2023, in close cooperation with agricultural experts from the highly regarded Regional Horticulture Research Station in Lebukhali, Patuakhali.http://www.sciencedirect.com/science/article/pii/S2352340924001537Image recognitionAgricultureWatermelon datasetDeep learningComputer vision |
| spellingShingle | Mohammad Imtiaz Nakib M.F. Mridha Comprehensive watermelon disease recognition dataset Data in Brief Image recognition Agriculture Watermelon dataset Deep learning Computer vision |
| title | Comprehensive watermelon disease recognition dataset |
| title_full | Comprehensive watermelon disease recognition dataset |
| title_fullStr | Comprehensive watermelon disease recognition dataset |
| title_full_unstemmed | Comprehensive watermelon disease recognition dataset |
| title_short | Comprehensive watermelon disease recognition dataset |
| title_sort | comprehensive watermelon disease recognition dataset |
| topic | Image recognition Agriculture Watermelon dataset Deep learning Computer vision |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924001537 |
| work_keys_str_mv | AT mohammadimtiaznakib comprehensivewatermelondiseaserecognitiondataset AT mfmridha comprehensivewatermelondiseaserecognitiondataset |