A dataset for vineyard disease detection via multispectral imaging.

Abstract

The present dataset is a collection of multispectral images designed for development of detection algorithms for grapevine diseases like Flavescence dorée (FD) and Esca (ED). Although FD severely threatens viticulture, there are few public datasets and none with multispectral data collected in the field. The collected images have been taken from a frontal perspective of vineyard plants that highlights details of leaves and trunks facilitating detailed disease analysis. The data were collected using a Micasense RedEdge-P multispectral camera, capturing six spectral bands across 172 image captures of three different grapevine varieties used in Lambrusco wines: Ancellotta, Marani, and Salamino. The dataset includes raw and processed images, calibration images for the multispectral camera, annotations detailing plant health conditions, and Python-based usage examples for researchers. Potential applications include the development of machine learning algorithms for automated disease detection, image alignment techniques, and background removal methods. The dataset is a valuable resource for advancing remote and proximal sensing in precision agriculture.

Plant phenotyping relevance

ブドウ樹の病害状態を対象とするマルチスペクトル画像データセットで、画像・校正・アノテーション・利用例を含む再利用可能な資源として構築されており、植物フェノタイピング手法の基盤が中心です。

Code and data availability

The paper is a data descriptor for a multispectral vineyard disease detection dataset deposited by the authors on Zenodo, including raw/processed images, annotations, and Python usage examples. The two Micasense GitHub repositories are generic vendor libraries, not paper-specific assets.