CCMT：作物病虫害检测数据集。

CCMT: Dataset for crop pest and disease detection.

作者信息

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

机构信息

Department of Computer Science and Informatics, University Energy and Natural Resources, Sunyani, Ghana.

Department of Horticulture and Crop Production, University Energy and Natural Resources, Sunyani, Ghana.

出版信息

Data Brief. 2023 Jun 12;49:109306. doi: 10.1016/j.dib.2023.109306. eCollection 2023 Aug.

DOI:10.1016/j.dib.2023.109306

PMID:37360671

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10285554/

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.

摘要

人工智能（AI）最近在农业领域已很明显。人工智能在农业中的目标是控制农作物病虫害、降低成本并提高作物产量。在发展中国家，农业部门面临诸多挑战，包括农民与技术之间的知识差距、病虫害侵扰、缺乏储存设施等。为了解决其中一些挑战，本文展示了源自加纳当地农场的农作物病虫害数据集。该数据集有两部分；原始图像包含24,881张图像（6,549张腰果、7,508张木薯、5,389张玉米和5,435张番茄），增强图像进一步分为训练集和测试集。后者包含102,976张图像（25,811张腰果、26,330张木薯、23,657张玉米和27,178张番茄），分为22类。所有图像均已去除身份标识，经专业植物病毒学家验证，可供研究团体免费使用。