Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: A systematic literature review

Daoliang Li, Shuai Yang, Zhuangzhuang Du, Xianbao Xu, Pan Zhang, Prof. Dr. Kang Yu, Jingcheng Zhang, Meiyan Shu
January, 2024
DOI

Abstract

Nitrogen (N) is an essential nutrient that profoundly impacts crop growth, yield, and overall quality. Misapplication of N fertilizers can negatively affect crop productivity and lead to adverse environmental outcomes. Given this challenge, unmanned aerial vehicle (UAV) remote sensing has steadily evolved into a cost-effective substitute for conventional destructive field sampling and laboratory analysis when determining crops’ N status. This paper presents a comprehensive literature review synthesising current and potential physiological and spectral indicators for assessing crop N status and comparing their respective attributes. Moreover, it scrutinizes the advantages and disadvantages of four distinct categories of crop N retrieval methodologies. This study described the processing flow of UAV images and compared the advantages and disadvantages of different UAV platforms and photogrammetry software. Additionally, the study analyzes the applications and developmental prospects of commonly employed UAV optical sensorsand multi-sensor information fusion technologies in monitoring crop N status. Finally, the paper delves into the primary challenges and prospective directions concerning assessing crop N status via UAV optical remote sensing. The purpose of this research review extends beyond offering theoretical and technical backing; it seeks to guide practical implementation strategies to foster the adoption and effective use of UAV optical remote sensing in evaluating crop N status.

Type
2
Publication
Computers and Electronics in Agriculture

Daoliang Li, Shuai Yang, Zhuangzhuang Du, Xianbao Xu, Pan Zhang, Kang Yu, Jingcheng Zhang, & Meiyan Shu (2024). Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: A systematic literature review. Computers and Electronics in Agriculture, 227: 109565.

Nitrogen Remote Sensing Unmanned Aerial Vehicle Winter Wheat Precision Agriculture Leaf Chlorophyll Content PLANT HEIGHT Crop Nitrogen Retrieval Methodologies Multi-Sensor Information Fusion HYPERSPECTRAL VEGETATION INDEXES AREA INDEX NONDESTRUCTIVE ESTIMATION PIGMENT CONTENT UAV-BASED RGB YIELD PREDICTION
Shuai Yang
Shuai Yang
PhD student

My research focuses on Deep learning, hyperspectral remote sensing, and radiative transfer model.

Prof. Dr. Kang Yu
Prof. Dr. Kang Yu
Professor of Precision Agriculture

My research interests include precision crop farming, hyperspectral remote sensing, and AI in agriculture.

Jingcheng Zhang
Jingcheng Zhang
PhD student

My research focuses on UAV-based phenotyping, hyperspectral remote sensing, and modeling of photosynthetic traits and yield in winter wheat.