A comprehensive review of remote sensing platforms, sensors, and applications in nut crops

遥感 足迹 螺母 分析 精准农业 计算机科学 农业工程 农业 环境科学 工程类 数据科学 地理 结构工程 考古
作者
Hamid Jafarbiglu,Alireza Pourreza
出处
期刊:Computers and Electronics in Agriculture [Elsevier]
卷期号:197: 106844-106844 被引量:116
标识
DOI:10.1016/j.compag.2022.106844
摘要

Due to their high protein content, nuts (almond, walnut, and pistachio) are among the main substitutes for meat, with a growing share of the food basket in the United States. However, the rapidly growing acreage of these crops, new legislations, the necessity of minimizing the environmental footprint, and a cost-effective production demand certain managerial practices based on precision agriculture and remote sensing, which have shown promising results in food production. This paper presents a comprehensive review of remote sensing platforms, sensors, applications, and analytic pipelines with a focus on nut crops, even though the materials are applicable for other specialty crops. In this regard, the paper is divided into five main sections: First, the problems and potential solutions are elaborated in the introduction. Second, the available platforms: satellites, manned aircraft, and UASs are discussed. Then the sensors used for remote sensing, their working principle, and the pros and cons of each are presented. Next, practiced and suggested applications of remote sensing data are reviewed. Finally, data processing and analytics needed to produce and interpret reliable results are highlighted. Key findings are listed as: 1) The acreage of the nut orchards and the purpose of the studies determine the fitting sensor and platform. 2) Although various sensors are available and reported to have promising results in other crops, they have not been used for nut crops. 3) Accurate sensor calibration is crucial for repeatable results as well as temporal and inter-field comparisons. 4) Except for water management, most remote sensing applications are limitedly studied in nut orchards, creating some research opportunities. 5) Finally, increasing data size requires new machine learning techniques and data fusion frameworks to handle all variables and fill the knowledge gap.
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