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.近年来，人工智能（Artificial Intelligence，以下简称AI）在农业领域的应用已愈发显著。人工智能在农业中的核心目标为提升作物产量、防控作物病虫害以及降低生产成本。发展中国家的农业部门面临诸多严峻挑战，包括病虫害侵袭、农民与技术之间的知识鸿沟以及仓储设施匮乏等问题。为助力解决部分此类挑战，本研究构建了源自加纳当地农场的作物病虫害数据集。该数据集分为两个子集：原始图像集与增强图像集。其中原始图像集共包含24881张图像，涵盖腰果（6549张）、木薯（7508张）、玉米（5389张）以及番茄（5435张）四类作物；增强图像集则进一步划分为训练集与测试集，总计102976张图像，涵盖腰果（25811张）、木薯（26330张）、玉米（23657张）以及番茄（27178张），共分为22个类别。所有图像均已完成去标识化处理，经植物病毒学专家验证，并向科研群体免费开放使用。