机器学习
计算机科学
人工智能
软件部署
深度学习
数据科学
斯科普斯
产量(工程)
领域(数学)
精准农业
钥匙(锁)
农业
科学网
荟萃分析
数学
地理
梅德林
软件工程
医学
内科学
材料科学
考古
计算机安全
政治学
纯数学
法学
冶金
作者
Nicoleta Darra,Evangelos Anastasiou,Olga Kriezi,Erato Lazarou,Dionissios Kalivas,Spyros Fountas
出处
期刊:Agronomy
[MDPI AG]
日期:2023-09-21
卷期号:13 (9): 2441-2441
被引量:11
标识
DOI:10.3390/agronomy13092441
摘要
Going beyond previous work, this paper presents a systematic literature review that explores the deployment of satellites, drones, and ground-based sensors for yield prediction in agriculture. It covers multiple aspects of the topic, including crop types, key sensor platforms, data analysis techniques, and performance in estimating yield. To this end, datasets from Scopus and Web of Science were analyzed, resulting in the full review of 269 out of 1429 retrieved publications. Our study revealed that China (93 articles, >1800 citations) and the USA (58 articles, >1600 citations) are prominent contributors in this field; while satellites were the primary remote sensing platform (62%), followed by airborne (30%) and proximal sensors (27%). Additionally, statistical methods were used in 157 articles, and model-based approaches were utilized in 60 articles, while machine learning and deep learning were employed in 142 articles and 62 articles, respectively. When comparing methods, machine learning and deep learning methods exhibited high accuracy in crop yield prediction, while other techniques also demonstrated success, contingent on the specific crop platform and method employed. The findings of this study serve as a comprehensive roadmap for researchers and farmers, enabling them to make data-driven decisions and optimize agricultural practices, paving the way towards a fully digitized yield prediction.
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