Grapes leaf disease dataset for precision agricultureMendeley Data
Grapes are widely cultivated fruit crops, essential for fresh consumption, winemaking and dried product production. However, their yield and quality are significantly impacted by various fungal diseases. This paper provides a large dataset of 2,726 high-quality grape leaf disease images collected fr...
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Elsevier
2025-08-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925004445 |
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| author | Madhuri Dharrao Nilima Zade R. Kamatchi Rakesh Sonawane Rabinder Henry Deepak Dharrao |
| author_facet | Madhuri Dharrao Nilima Zade R. Kamatchi Rakesh Sonawane Rabinder Henry Deepak Dharrao |
| author_sort | Madhuri Dharrao |
| collection | DOAJ |
| description | Grapes are widely cultivated fruit crops, essential for fresh consumption, winemaking and dried product production. However, their yield and quality are significantly impacted by various fungal diseases. This paper provides a large dataset of 2,726 high-quality grape leaf disease images collected from grapes farm of Nashik, India in two years of span 2023 to 2025. The dataset is precisely annotated under the guidance and observation of agriculture domain expert and organized in a well-defined folder structure. The dataset captures the two major categories healthy leaves and unhealthy leaves, during cultivation period. A primary directory containing two main classes Heathy Leaf Images and Unhealthy Leaf images. Further unhealthy class is divided into three subfolders for disease class, namely Downy Mildew, Powdery Mildew and Bacterial Leaf Spot. These are the major fungal disease observed on grape crop causes substantially crop losses and ultimately impact on the yield production. Timely identification of these diseases can significantly reduce the risk of crop loss and help to improve quality of fruit with maximum yield production. This High-quality annotated image dataset can help to design standard advanced AI models for automated disease detection, classification, and prediction. The dataset was validated through a transfer learning approach using the ResNet-18 algorithm and demonstrated the remarkable classification accuracy of 96 %. These results validate the dataset’s quality and its suitability for deep learning-based grape disease detection. Overall, this open-access resource provides a valuable foundation for computer vision, machine learning, and agricultural technology researchers aims to enhance disease management practices in grape production. thus, this is an effective source of data for future studies and real-world applications in sustainable grape production. |
| format | Article |
| id | doaj-art-ede85039dfaa479982c5b44fd3a595aa |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-ede85039dfaa479982c5b44fd3a595aa2025-08-20T03:57:32ZengElsevierData in Brief2352-34092025-08-016111171610.1016/j.dib.2025.111716Grapes leaf disease dataset for precision agricultureMendeley DataMadhuri Dharrao0Nilima Zade1R. Kamatchi2Rakesh Sonawane3Rabinder Henry4Deepak Dharrao5Department of Computer Science & Engineering, Symbiosis Institute of Technology Pune, Symbiosis International (Deemed University), Pune 412115, Maharashtra, IndiaDepartment of Computer Science & Engineering, Symbiosis Institute of Technology Pune, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India; Corresponding author.Universal SkillTech University, Mumbai, 401208, Maharashtra, IndiaMahatma Phule Krishi Vidyapeeth Rahuri Dist.Ahilynagar (Onion Grape Research Station, Pimpalgaon Baswant, Nashik), 422209, Maharashtra, IndiaAtlas Skilltech University, Mumbai, 400070, Maharashtra, IndiaDepartment of Computer Science & Engineering, Symbiosis Institute of Technology Pune, Symbiosis International (Deemed University), Pune 412115, Maharashtra, IndiaGrapes are widely cultivated fruit crops, essential for fresh consumption, winemaking and dried product production. However, their yield and quality are significantly impacted by various fungal diseases. This paper provides a large dataset of 2,726 high-quality grape leaf disease images collected from grapes farm of Nashik, India in two years of span 2023 to 2025. The dataset is precisely annotated under the guidance and observation of agriculture domain expert and organized in a well-defined folder structure. The dataset captures the two major categories healthy leaves and unhealthy leaves, during cultivation period. A primary directory containing two main classes Heathy Leaf Images and Unhealthy Leaf images. Further unhealthy class is divided into three subfolders for disease class, namely Downy Mildew, Powdery Mildew and Bacterial Leaf Spot. These are the major fungal disease observed on grape crop causes substantially crop losses and ultimately impact on the yield production. Timely identification of these diseases can significantly reduce the risk of crop loss and help to improve quality of fruit with maximum yield production. This High-quality annotated image dataset can help to design standard advanced AI models for automated disease detection, classification, and prediction. The dataset was validated through a transfer learning approach using the ResNet-18 algorithm and demonstrated the remarkable classification accuracy of 96 %. These results validate the dataset’s quality and its suitability for deep learning-based grape disease detection. Overall, this open-access resource provides a valuable foundation for computer vision, machine learning, and agricultural technology researchers aims to enhance disease management practices in grape production. thus, this is an effective source of data for future studies and real-world applications in sustainable grape production.http://www.sciencedirect.com/science/article/pii/S2352340925004445Grapes disease datasetComputer visionMachine learningDisease classificationPrecision agriculture |
| spellingShingle | Madhuri Dharrao Nilima Zade R. Kamatchi Rakesh Sonawane Rabinder Henry Deepak Dharrao Grapes leaf disease dataset for precision agricultureMendeley Data Data in Brief Grapes disease dataset Computer vision Machine learning Disease classification Precision agriculture |
| title | Grapes leaf disease dataset for precision agricultureMendeley Data |
| title_full | Grapes leaf disease dataset for precision agricultureMendeley Data |
| title_fullStr | Grapes leaf disease dataset for precision agricultureMendeley Data |
| title_full_unstemmed | Grapes leaf disease dataset for precision agricultureMendeley Data |
| title_short | Grapes leaf disease dataset for precision agricultureMendeley Data |
| title_sort | grapes leaf disease dataset for precision agriculturemendeley data |
| topic | Grapes disease dataset Computer vision Machine learning Disease classification Precision agriculture |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925004445 |
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