Dataset for Crop Pest and Disease Detection
The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The main goal of AI in agriculture is to improve crop yield, control crop pests/diseases, and reduce cost. The agricultural sector in developing countries faces severe in the form of disease and pest infestation, the knowledge gap between farmers and technology, and a lack of storage facilities, among others. To help address some of these challenges, this work 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 set 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.
Steps to reproduce
The Crop pest and disease datasets were collected using a high-resolution camera device. The original .jpg images were in varied dimensions, namely; (400x400), (487 x 1080), (1080x518), (3024x4032), and (4032x3024). There are 22 classes in total. Cashew has 5 classes: anthracnose, gummosis, healthy, leaf miner, and red rust. Cassava has 5 classes: bacterial blight, brown spot, green mite, healthy, and mosaic. Maize has 7 classes: fall armyworm, grasshopper, healthy, leaf beetle, leaf blight, leaf spot, and streak virus. Tomato also has 5 classes: healthy, leaf blight, leaf curl, septoria leaf spot, and verticillium wilt. The images were captured under various conditions and with different backgrounds such as white, dark, illuminated, and real backgrounds.
African Technology Society Policy Network