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<p>We introduce the Grapevine (Vitis vinifera) Leaf Image Dataset (GVLiD), a carefully curated set of 3,477 annotated images of Grapevine (Vitis vinifera) leaves to catalyze research in computer vision and plant pathology. Whereas the PlantVillage and Hermos datasets, for instance, contain mainly scanned or laboratory-acquired leaves, GVLiD features vineyard <i>in situ</i> images along with detailed metadata (GPS, lighting, weather, and device model) and expert-verified annotations. To measure the reliability of the annotation, label consistency was very high (κ = 0.86–0.92; 95% CI) as assessed by inter- and intra-rater agreement. Besides Indian viticulture, the dataset also aims to support the field of foliar disease detection in precision agriculture and ML benchmarking, which face significant challenges due to variable illumination and natural leaf backgrounds under field conditions. GVLiD is intended to enable worldwide, reproducible, real-world testing of AI systems for crop disease monitoring.</p>

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A curated dataset of 3,477 high-resolution Grapevine (Vitis vinifera) leaf images for automated detection of Black Rot, Esca, and Leaf Blight diseases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Milind Gayakwad,
  • Tahseen Mulla,
  • Deepak Parashar,
  • Anisa Shikalgar,
  • Prashant Chavan,
  • Sachin Wakurdekar,
  • Rohini Jadhav,
  • Amol Kadam,
  • Priyanka Paygude,
  • Rahul Joshi

摘要

We introduce the Grapevine (Vitis vinifera) Leaf Image Dataset (GVLiD), a carefully curated set of 3,477 annotated images of Grapevine (Vitis vinifera) leaves to catalyze research in computer vision and plant pathology. Whereas the PlantVillage and Hermos datasets, for instance, contain mainly scanned or laboratory-acquired leaves, GVLiD features vineyard in situ images along with detailed metadata (GPS, lighting, weather, and device model) and expert-verified annotations. To measure the reliability of the annotation, label consistency was very high (κ = 0.86–0.92; 95% CI) as assessed by inter- and intra-rater agreement. Besides Indian viticulture, the dataset also aims to support the field of foliar disease detection in precision agriculture and ML benchmarking, which face significant challenges due to variable illumination and natural leaf backgrounds under field conditions. GVLiD is intended to enable worldwide, reproducible, real-world testing of AI systems for crop disease monitoring.