Asante Emmanuel, Appiah Obed, Appiahene Peter, Adu Kwabena
Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani.
Data Brief. 2024 Mar 4;54:110261. doi: 10.1016/j.dib.2024.110261. eCollection 2024 Jun.
Hyperspectral imaging, combined with deep learning techniques, has been employed to classify maize. However, the implementation of these automated methods often requires substantial processing and computing resources, presenting a significant challenge for deployment on embedded devices due to high GPU power consumption. Access to Ghanaian local maize data for such classification tasks is also extremely difficult in Ghana. To address these challenges, this research aims to create a simple dataset comprising three distinct types of local maize seeds in Ghana. The goal is to facilitate the development of an efficient maize classification tool that minimizes computational costs and reduces human involvement in the process of grading seeds for marketing and production. The dataset is presented in two parts: raw images, consisting of 4,846 images, are categorized into bad and good. Specifically, 2,211 images belong to the bad class, while 2,635 belong to the good class. Augmented images consist of 28,910 images, with 13,250 representing bad data and 15,660 representing good data. All images have been validated by experts from Heritage Seeds Ghana and are freely available for use within the research community.
高光谱成像与深度学习技术相结合，已被用于对玉米进行分类。然而，这些自动化方法的实施通常需要大量的处理和计算资源，由于GPU功耗高，这对在嵌入式设备上部署构成了重大挑战。在加纳，获取用于此类分类任务的加纳本地玉米数据也极为困难。为应对这些挑战，本研究旨在创建一个简单的数据集，该数据集包含加纳三种不同类型的本地玉米种子。目标是促进开发一种高效的玉米分类工具，该工具可将计算成本降至最低，并减少种子分级用于销售和生产过程中的人工参与。该数据集分为两部分：原始图像由4846张图像组成，分为坏的和好的两类。具体而言，2211张图像属于坏类，而2635张属于好类。增强图像由28910张图像组成，其中13250张代表坏数据，15660张代表好数据。所有图像均经过加纳传统种子专家的验证，可供研究社区免费使用。
