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Related Experiment Video

Updated: Jun 22, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

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

Emmanuel Asante¹, Obed Appiah¹, Peter Appiahene¹

¹Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani.

|July 4, 2024

Summary

This summary is machine-generated.

This study introduces a new, accessible dataset of Ghanaian local maize seeds to enable efficient, low-cost seed classification. The goal is to support the development of tools that reduce computational demands and human effort in seed grading.

Keywords:

Convolutional Neural Network Image recognition Machine learning Machine learning algorithm Maize dataset Precision agriculture

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Published on: October 9, 2018

Area of Science:

Agricultural Science
Computer Vision
Data Science

Background:

Hyperspectral imaging and deep learning are used for maize classification but require significant computational resources, limiting deployment on embedded systems.
High GPU power consumption and limited access to local Ghanaian maize data pose challenges for developing efficient classification tools in Ghana.

Purpose of the Study:

To create a simple, accessible dataset of local Ghanaian maize seeds for developing efficient classification tools.
To minimize computational costs and reduce human involvement in grading maize seeds for marketing and production.

Main Methods:

The research involved creating a dataset of raw and augmented images of three types of local maize seeds from Ghana.
The raw image dataset contains 4,846 images (2,211 'bad', 2,635 'good'), and the augmented dataset has 28,910 images (13,250 'bad', 15,660 'good').

Main Results:

A validated dataset of Ghanaian local maize seeds has been created, comprising raw and augmented images.
The dataset is categorized into 'bad' and 'good' quality seeds, with expert validation from Heritage Seeds Ghana.

Conclusions:

The developed dataset facilitates the creation of computationally efficient maize classification tools.
This resource aims to overcome data accessibility issues in Ghana and reduce the need for extensive computational power and human intervention in seed grading.