w.uenr.edu.gh African Technology Policy Society Network 8 th Floor – The Chancery – Valley Road - Nairobi P.O. Box 10081-00100, Nairobi, Kenya Website: http://www.atpsnet.org Data accessibility Repository name: Dataset for Crop Pest and Disease Detection Data identification number(doi): 10.17632/bwh3zbpkpv.1 Direct URL to data: https://data.mendeley.com/datasets/bwh3zbpkpv Value of the Data • The dataset is comprehensive and consists of 102,976 high-quality images of four crops with 22 different classes, respectively cashew (5 classes), cassava (5 classes), maize (7 classes), and tomato (5 classes). • The dataset consists of plant leaves, pests, fruits and images of sick parts of cashew, cas
Open resource ↗10.17632/bwh3zbpkpv.1 · lines:1-60Paper record
CCMT: Dataset for crop pest and disease detection.
Data in brief · 12 Jun 2023 · 10.1016/j.dib.2023.109306
Abstract
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.
Code and data availability