CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques.

Abstract

Around the world, citrus production and quality are threatened by diseases caused by fungi, bacteria, and viruses. Citrus growers are currently demanding technological solutions to reduce the economic losses caused by citrus diseases. In this context, image analysis techniques have been widely used to detect citrus diseases, extracting discriminant features from an input image to distinguish between healthy and abnormal cases. The dataset presented in this article is helpful for training, validating, and comparing citrus abnormality detection algorithms. The data collection comprises 953 color images taken from the orange leaves of Citrus sinensis (L.) Osbeck species. There are 12 nutritional deficiencies and diseases supporting the development of automatic detection methods that can reduce economic losses in citrus production.

Plant phenotyping relevance

柑橘葉の異常・病害を画像で検出するためのデータセットで、検出アルゴリズムの訓練・検証・比較を主目的としており、植物フェノタイピング手法が中心です。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.