Seven Years of Innovation: A Comprehensive, Retrospective View of a Cable-Suspended Field Plant Phenotyping System (NU - Spidercam) and Its Role in Precision Agriculture

Abstract

Highlights This study presents a seven-year operational review of a large-scale cable-suspended phenotyping system. Standardized operation and data protocols enable the delivery of high-resolution, high-frequency phenotypic datasets. The platform supports a wide range of phenotyping applications, from morphological to physiological trait analysis. The system plays a critical role in developing advanced sensing technology for phenotyping and precision agriculture. ABSTRACT. High-throughput plant phenotyping (HTPP) significantly improves plant phenotyping efficiency by integrating advanced sensing technologies, data processing, and modeling techniques. Over the last two decades, field-based HTPP systems have evolved from handheld sensors to sophisticated robotic platforms. Large-scale, ground-based phenotyping facilities have made significant contributions to advancing technology through their high sensor payloads, proximal measurement capabilities, and unmatched spatial and temporal resolution. This review paper presents a detailed and quantitative analysis of the operational performance of the cable-suspended NU-Spidercam phenotyping facility at the University of Nebraska–Lincoln from 2017 to 2023, focusing on daily operations, data management strategies, and system maintenance. Additionally, the paper systematically summarizes representative studies performed at the facility across morphological, spectral, and physiological phenotyping domains. Finally, the discussion highlights future directions, emphasizing the NU-Spidercam’s role in validating mobile phenotyping platforms, enabling precision agriculture research, supporting fundamental remote sensing studies, and facilitating the transfer of advanced sensing techniques to affordable, mobile platforms such as drones. Keywords: Artificial intelligence, Computer vision, Deep learning, Field plant phenotyping, Large-scale facility, Machine learning, Operational review, Physiological phenotyping.

Plant phenotyping relevance

大規模なケーブル懸架型植物フェノタイピング施設の運用性能、データ管理、保守、および形態・スペクトル・生理形質測定を体系的にレビューしており、フェノタイピング基盤が中心である。

Code and data availability

This is a retrospective review of the NU-Spidercam facility. It describes internal data management (three-copy backups, CSV trait files) but provides no public dataset, image, code, or model deposit with an authors' URL. The nemesonet.unl.edu link is a general weather-station service, not a paper-specific asset; the Ph

No evidence-backed public reproduction asset is currently recorded.