# PlantDoc: A Dataset for Visual Plant Disease Detection

arXiv: https://arxiv.org/abs/1911.10317 · HTML: https://mlsanity.com/paper/1911.10317/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2019.

## Built on by (most cited first, 30 of 68)

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- [PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science](https://mlsanity.com/paper/2508.17117/) (2025)
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- [CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture](https://mlsanity.com/paper/2305.10084/) (2023)
- [Machine Learning for Leaf Disease Classification: Data, Techniques and Applications](https://mlsanity.com/paper/2310.12509/) (2023)
- [Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches](https://mlsanity.com/paper/2310.16273/) (2023)
- [CLIP Adaptation by Intra-modal Overlap Reduction](https://mlsanity.com/paper/2409.11338/) (2024)
- [PND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Network](https://mlsanity.com/paper/2410.12742/) (2024)

## Abstract (arXiv)

India loses 35% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. In this paper, we explore the possibility of computer vision approaches for scalable and early plant disease detection. The lack of availability of sufficiently large-scale non-lab data set remains a major challenge for enabling vision based plant disease detection. Against this background, we present PlantDoc: a dataset for visual plant disease detection. Our dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. To show the efficacy of our dataset, we learn 3 models for the task of plant disease classification. Our results show that modelling using our dataset can increase the classification accuracy by up to 31%. We believe that our dataset can help reduce the entry barrier of computer vision techniques in plant disease detection.