CCMT: Dataset for crop pest and disease detection - Patrick Kwabena Mensah; Vivian Akoto-Adjepong; Kwabena Adu; Mighty Abra Ayidzoe; Elvis Asare Bediako; Owusu Nyarko-Boateng; Samuel Boateng; Esther Fobi Donkor; Faiza Umar Bawah; Nicodemus Songose Awarayi; Peter Nimbe; Isaac Kofi Nti; et al. (Data in Brief, 2023)
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.