Plant disease is a common impediment to the productivity of the world's agricultural production, which adversely affects the quality and yield of crops and causes heavy economic losses to farmers. The cucumber is a frequently grown creeping vine plant that has few calories but is high in water and several vital vitamins and minerals. Due to the unfavorable ecological environment and non-biological circumstances, cucumber diseases will adversely harm the quality of cucumber and cause heavy financial loss. Early identification and protection of crop diseases are essential for disease management, crop yield enhancement, cost reduction, and boosting agricultural production. The traditional diagnosis of crop diseases is often time-consuming, laborious, ineffective, and subjective. To cope with this scenario, the development of a machine-based model which can detect cucumber diseases is a demand of time for increasing agricultural production. This article offers a major cucumber dataset to build an effective machine vision-based model which can detect more variety of cucumber diseases. The dataset includes eight different types of classes containing disease-affected and disease-free cucumber images (Anthracnose, Bacterial Wilt, Belly Rot, Downy Mildew, Pythium Fruit Rot, Gummy Stem Blight, Fresh leaves, and Fresh cucumber) which were collected from the 6th to 30th of May 2022 from real fields with the cooperation of an expert from an agricultural institution. The dataset is hosted by the Department of Computer Science and Engineering, Jahangirnagar University, and is freely accessible at https://data.mendeley.com/datasets/y6d3z6f8z9/1.
Keywords: Agriculture; Computer vision; Cucumber dataset; Deep learning; Image recognition.
© 2023 The Author(s).