ArticleNucleic acids research2024
PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Leaf-DETR: Progressive adaptive network with lower matching cost for dense leaves detection.Plant phenomics (Washington, D.C.) · 2026Article
- OpenPlant: A Large-Scale Benchmark Dataset for Agricultural Plant Classification Using CNNs, ViTs, and VLMs.Plants (Basel, Switzerland) · 2026Article
- Multi-objective Big Bang Big Crunch framework for reliable rice disease and variety classification with conditional calibration.PloS one · 2026Article
- LMP-PM: a lightweight multi-path pruning method for plant leaf disease recognition.Frontiers in plant science · 2026Article
- Segmenting vegetation from UAV images via spectral reconstruction in complex field environments.Plant phenomics (Washington, D.C.) · 2025Article
- PlantCaFo: An efficient few-shot plant disease recognition method based on foundation models.Plant phenomics (Washington, D.C.) · 2025Article
- Soft prompt-tuning for plant pest and disease classification from colloquial descriptions.Frontiers in plant science · 2025Article
- The 2024 Nucleic Acids Research database issue and the online molecular biology database collection.Nucleic acids research · 2024Article
- Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition.Plant phenomics (Washington, D.C.) · 2024Article
- PlanText: Gradually Masked Guidance to Align Image Phenotypes with Trait Descriptions for Plant Disease Texts.Plant phenomics (Washington, D.C.) · 2024Article
- Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.Plant phenomics (Washington, D.C.) · 2024Article
- CSNet: A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting.Plant phenomics (Washington, D.C.) · 2024Article
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 crops and 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease education and efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managers and pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.
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Registered trials
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