An India soyabean dataset for identification and classification of diseases using computer-vision algorithms.

Abstract

Intelligent agriculture heavily relies on the science of agricultural disease image recognition. India is also responsible for large production of French beans, accounting for 37.25% of total production. In India from south region of Maharashtra state this crop is cultivated thrice in year. Soyabean plant is planted between the months of June through July, during the months of October and September during the rabi season, as well as in February. In the Maharashtrian regions of Pune, Satara, Ahmednagar, Solapur, and Nashik, among others, Soyabean plant is a common crop. In Maharashtra, Soyabean plant is grown over an area of around 31,050 hectares. This research presents a dataset of leaves from soyabean plants that are both insect-damaged and healthy. Images were taken over the course of fewer than two to three seasons on several farms. There are 3363 photos altogether in the seven folders that make up the dataset. Six categories comprise the dataset: I) Healthy plants II) Vein Necrosis III) Dry leaf IV) Septoria brown spot V) Root images VI) Bacterial leaf blight. This study's goal is to give academics and students accessibility to our dataset so they may use it for their studies and to build machine learning models.

Plant phenotyping relevance

ダイズ葉の健全・病害状態を画像データセットとして構築し、機械学習による植物病害の識別・分類に利用可能にする研究であり、表現型取得用データセットが中心である。

Code and data availability

The paper is a Data in Brief article describing a public soyabean leaf disease image dataset (3363 images, six classes) deposited on Mendeley Data with an explicit DOI and direct URL. This is a paper-specific, publicly available plant phenotyping asset (raw and preprocessed leaf images). No separate analysis code asset