Integrating Multi-Angle and Multi-Scale Remote Sensing for Precision Nitrogen Management in Agriculture: A Review
Computers and Electronics in Agriculture（2025）
Department of Agroecology
Abstract
Nitrogen (N) is an essential element for crop growth, productivity, and quality, making it a fundamental component of crop nutrition. In precision agriculture, rapid and non-destructive monitoring of crop N status is crucial for formulating N management strategies to optimize N application and assessing crop performance. This review investigates the integration of remote sensing (RS) in precision N management, particularly focusing on addressing temporal, scale, and geometric consideration in RS applications. The study reviews RS monitoring techniques from three perspectives: firstly, determining optimal fertilization timing based on crop phenology; secondly, introducing RS platforms, including proximal sensing, airborne RS, and satellites for monitoring crop N status; and finally, examining the use of multi-angle RS techniques for N monitoring. The literature reviewed in this study shows that 29% of publications focus on N monitoring at joining and 24% at grain-filling stage, limiting the window for making decisions for in-season N management. This paper concludes that integrating appropriate monitoring platforms, multi-angle observations, and dynamic modeling offers a promising approach for assessing crop N status. This integrated approach provides an essential decision-making tool for N fertilization, advancing precision agriculture for its broader implication. Advancing dynamic crop models, in-field digital twins, multi-scale RS for seamless monitoring, and artificial intelligence for real-time N status diagnosis together will pave the way for precision N management in modern agriculture.
MoreTranslated text
Key words
Nitrogen diagnosis,Nitrogen monitoring,Nitrogen scheduling,Spatio-temporal variability,Crop phenology,Decision support system
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined