{
 "id": "enalis/LeafNet",
 "owner": "enalis",
 "name": "LeafNet",
 "downloads": 119,
 "likes": 21,
 "last_modified": "2026-03-13T07:48:54.000Z",
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 "licence_name": null,
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 "sources": [],
 "gated": "auto",
 "tasks": [
  "image-classification",
  "image-to-text"
 ],
 "citation": "@article{NGUYENQUOC2025130084,\ntitle = {A Vision-Language Foundation Model for Leaf Disease Identification},\njournal = {Expert Systems with Applications},\npages = {130084},\nyear = {2025},\nissn = {0957-4174},\ndoi = {https://doi.org/10.1016/j.eswa.2025.130084},\nurl = {https://www.sciencedirect.com/science/article/pii/S0957417425037005},\nauthor = {Khang {Nguyen Quoc} and Lan Le {Thi Thu} and Luyl-Da Quach},\nkeywords = {Leaf disease identification, Contrastive learning, Vision-language models, Foundation models, Image-text retrieval, Context-aware learning},\nabstract = {Leaf disease identification plays a pivotal role in smart agriculture. However, many existing studies still struggle to integrate image and textual modalities to compensate for each other’s limitations. Furthermore, many of these approaches rely on pretraining with constrained datasets such as ImageNet, which lack domain-specific information. The research proposes SCOLD (Soft-target COntrastive learning for Leaf Disease identification), a context-aware vision-language foundation model tailored to domain-specific tasks in smart agriculture. SCOLD is developed using a diverse corpus of plant leaf images and corresponding symptom descriptions, comprising over 186,000 image-captions pairs aligned with 97 unique concepts. Through task-agnostic pretraining, SCOLD leverages contextual soft targets to mitigate overconfidence in contrastive learning by smoothing labels, thereby improving model generalization and robustness",
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}