A survey on deep learning applications in wheat phenotyping

Precision farming has become a hot research topic in recent years due to the advancement of sensing technologies, increased computer performance, and advanced deep learning algorithms. As a result, several outstanding studies on deep learning applications to high-throughput phenotyping of wheat, one of the most demanding cereal crops on the planet, have been published. This paper aims to conduct a survey of publications that have used deep learning techniques to address various challenges in wheat production. To accomplish this, we propose an ontology-based knowledge management system that is specifically designed to highlight the publications' objectives, preprocessing algorithms, deep learning models, frameworks, datasets, and results. The presented ontology is intended to serve as a robust tool for future research in wheat high-throughput phenotyping. Additionally, we compare the performance of deep learning algorithms to that of long-established methods in this field. Compared to traditional machine learning techniques, this study demonstrates that deep learning algorithms provide a more robust, accurate, and cost-effective way of measuring wheat traits.Crown Copyright (c) 2022 Published by Elsevier B.V. All rights reserved.

Automated summary

Precision farming has gained significant attention in recent years due to advances in sensing technologies and deep learning algorithms. This paper presents a survey of publications that utilized deep learning techniques to address challenges in wheat production. The authors propose an ontology-based knowledge management system to highlight the objectives, algorithms, models, frameworks, datasets, and results of these publications. The study demonstrates that deep learning algorithms offer a more robust, accurate, and cost-effective approach for measuring wheat traits compared to traditional machine learning techniques.