# PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation

arXiv: https://arxiv.org/abs/2409.04038 · HTML: https://mlsanity.com/paper/2409.04038/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [Side Adapter Network for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2302.12242/) (2023)
- [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://mlsanity.com/paper/2209.08575/) (2022)
- [An open access repository of images on plant health to enable the development of mobile disease diagnostics](https://mlsanity.com/paper/1511.08060/) (2015)
- [PlantDoc: A Dataset for Visual Plant Disease Detection](https://mlsanity.com/paper/1911.10317/) (2019)
- [Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile Baseline](https://mlsanity.com/paper/2408.03120/) (2024)
- [Snap and Diagnose: An Advanced Multimodal Retrieval System for Identifying Plant Diseases in the Wild](https://mlsanity.com/paper/2408.14723/) (2024)

## Built on by (most cited first, 14 of 14)

- [SVIP: Semantically Contextualized Visual Patches for Zero-Shot Learning](https://mlsanity.com/paper/2503.10252/) (2025)
- [CF-PRNet: Coarse-to-Fine Prototype Refining Network for Point Cloud Completion and Reconstruction](https://mlsanity.com/paper/2409.08443/) (2024)
- [LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases](https://mlsanity.com/paper/2602.13662/) (2026)
- [Track Any Peppers: Weakly Supervised Sweet Pepper Tracking Using VLMs](https://mlsanity.com/paper/2411.06702/) (2024)
- [Augment to Segment: Tackling Pixel-Level Imbalance in Wheat Disease and Pest Segmentation](https://mlsanity.com/paper/2509.09961/) (2025)
- [Distributed Zero-Shot Learning for Visual Recognition](https://mlsanity.com/paper/2511.08170/) (2025)
- [Mobile-Friendly Deep Learning for Plant Disease Detection: A Lightweight CNN Benchmark Across 101 Classes of 33 Crops](https://mlsanity.com/paper/2508.10817/) (2025)
- [Beyond the Encoder: Joint Encoder-Decoder Contrastive Pre-Training Improves Dense Prediction](https://mlsanity.com/paper/2503.17526/) (2025)
- [AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock](https://mlsanity.com/paper/2507.22101/) (2025)
- [LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping](https://mlsanity.com/paper/2512.13130/) (2025)
- [Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices](https://mlsanity.com/paper/2601.02353/) (2026)
- [StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components](https://mlsanity.com/paper/2602.00703/) (2026)
- [Benchmarking Vision-Language Models for Microscopic Plant Image Understanding](https://mlsanity.com/paper/2606.22497/) (2026)
- [AppleScab-LT: A Longitudinal Real-Field Apple Scab Dataset for Temporal Disease Progression Analysis](https://mlsanity.com/paper/2608.14235/) (2026)

## Abstract (arXiv)

Plant diseases pose significant threats to agriculture. It necessitates proper diagnosis and effective treatment to safeguard crop yields. To automate the diagnosis process, image segmentation is usually adopted for precisely identifying diseased regions, thereby advancing precision agriculture. Developing robust image segmentation models for plant diseases demands high-quality annotations across numerous images. However, existing plant disease datasets typically lack segmentation labels and are often confined to controlled laboratory settings, which do not adequately reflect the complexity of natural environments. Motivated by this fact, we established PlantSeg, a large-scale segmentation dataset for plant diseases. PlantSeg distinguishes itself from existing datasets in three key aspects. (1) Annotation type: Unlike the majority of existing datasets that only contain class labels or bounding boxes, each image in PlantSeg includes detailed and high-quality segmentation masks, associated with plant types and disease names. (2) Image source: Unlike typical datasets that contain images from laboratory settings, PlantSeg primarily comprises in-the-wild plant disease images. This choice enhances the practical applicability, as the trained models can be applied for integrated disease management. (3) Scale: PlantSeg is extensive, featuring 11,400 images with disease segmentation masks and an additional 8,000 healthy plant images categorized by plant type. Extensive technical experiments validate the high quality of PlantSeg's annotations. This dataset not only allows researchers to evaluate their image classification methods but also provides a critical foundation for developing and benchmarking advanced plant disease segmentation algorithms.