CCMT: Dataset for crop pest and disease detection

Artificial Intelligence (AI) has been evident in the agricultural sector recently. The objective of AI in agriculture is to control crop pests/diseases, reduce cost, and improve crop yield. In developing countries, the agriculture sector faces numerous challenges in the form of knowledge gap between...

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Bibliographic Details
Published in:	Data in brief Vol. 49; p. 109306
Main Authors:	Mensah, Patrick Kwabena, Akoto-Adjepong, Vivian, Adu, Kwabena, Ayidzoe, Mighty Abra, Bediako, Elvis Asare, Nyarko-Boateng, Owusu, Boateng, Samuel, Donkor, Esther Fobi, Bawah, Faiza Umar, Awarayi, Nicodemus Songose, Nimbe, Peter, Nti, Isaac Kofi, Abdulai, Muntala, Adjei, Remember Roger, Opoku, Michael, Abdulai, Suweidu, Amu-Mensah, Fred
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier Inc 01-08-2023 Elsevier
Subjects:	Artificial Intelligence Classification Data Dataset: Deep learning Pest detection: Plant disease detection Dataset: Deep learning Pest detection: Plant disease detection Artificial Intelligence Classification
Online Access:	Get full text
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Summary:	Artificial Intelligence (AI) has been evident in the agricultural sector recently. The objective of AI in agriculture is to control crop pests/diseases, reduce cost, and improve crop yield. In developing countries, the agriculture sector faces numerous challenges in the form of knowledge gap between farmers and technology, disease and pest infestation, lack of storage facilities, among others. In order to resolve some of these challenges, this paper presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images (6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and augmented images which is further split into train and test sets. The latter consists of 102,976 images (25,811-Cashew, 26,330-Cassava, 23,657-Maize, and 27,178-Tomato), categorized into 22 classes. All images are de-identified, validated by expert plant virologists, and freely available for use by the research community.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2352-3409 2352-3409
DOI:	10.1016/j.dib.2023.109306