EfficientMaize: A Lightweight Dataset for Maize Classification on Resource-Constrained Devices

• Published in: Data in brief Vol. 54; p. 110261
• Main Authors: Asante, Emmanuel, Appiah, Obed, Appiahene, Peter, Adu, Kwabena
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01-06-2024; Elsevier
• Subjects: Convolutional Neural Network; Data; Image recognition; Machine learning; Machine learning algorithm; Maize dataset; Precision agriculture; Precision agriculture; Maize dataset; Convolutional Neural Network; Image recognition; Machine learning; Machine learning algorithm
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2024.110261

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.