Seed detection and classification with artificial intelligence: A review
School of Computing, Southern Illinois University Carbondale, Carbondale, IL 62901, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 508–522
Publication history:
Received on 25 July 2024; revised on 31 August 2024; accepted on 03 September 2024
Abstract:
Seed detection and classification play a crucial role in the agriculture domain, and artificial intelligence has been increasingly combined with agriculture in various sectors. Manual seed detection and classification are time-consuming and less accurate compared to automated methods. Several research works have been conducted on automatic seed detection and classification using machine learning and deep learning algorithms. This review examined ten experimental research works focusing on seed detection and classification using these algorithms. The approaches, contributions, datasets, data preprocessing, algorithms, results, and limitations of each work were reviewed and presented. The survey revealed that Convolutional Neural Networks (CNN) are the most frequently chosen algorithms for seed classification, with 93% of the reviewed works using CNN for comparing and evaluating their models. Based on the in-depth survey, four recommendations are made for consideration in future experimental analyses of seed detection and classification. These findings highlight the importance of artificial intelligence in advancing seed detection and classification techniques in the agriculture domain.
Keywords:
Seed classification survey; Seed detection review; Review on seed identification; Seed analysis; Review in agriculture
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0