Biological Systems Engineering, Department of


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BAI Geng, GE Yufeng. Crop stress sensing and plant phenotyping systems: A review[J]. Smart Agriculture, 2023, 5(1): 66-81. doi:10.12133/j.smartag.SA202211001 (in English with Chinese abstract)

白更, 葛玉峰. 作物胁迫感知和植物表型测量系统综述[J]. 智慧农业(中英文), 2023, 5(1): 66-81. DOI: 10.12133/j. smartag.SA202211001


Enhancing resource use efficiency in agricultural field management and breeding high-performance crop varieties are crucial approaches for securing crop yield and mitigating negative environmental impact of crop production. Crop stress sensing and plant phenotyping systems are integral to variable-rate (VR) field management and high-throughput plant phenotyping (HTPP), with both sharing similarities in hardware and data processing techniques. Crop stress sensing systems for VR field management have been studied for decades, aiming to establish more sustainable management practices. Concurrently, significant advancements in HTPP system development have provided a technological foundation for reducing conventional phenotyping costs. In this paper, we present a systematic review of crop stress sensing systems employed in VR field management, followed by an introduction to the sensors and data pipelines commonly used in field HTPP systems. State-of-the-art sensing and decision-making methodologies for irrigation scheduling, nitrogen application, and pesticide spraying are categorized based on the degree of modern sensor and model integration. We highlight the data processing pipelines of three ground-based field HTPP systems developed at the University of Nebraska-Lincoln. Furthermore, we discuss current challenges and propose potential solutions for field HTPP research. Recent progress in artificial intelligence, robotic platforms, and innovative instruments is expected to significantly enhance system performance, encouraging broader adoption by breeders. Direct quantification of major plant physiological processes may represent one of next research frontiers in field HTPP, offering valuable phenotypic data for crop breeding under increasingly unpredictable weather conditions. This review can offer a distinct perspective, benefiting both research communities in a novel manner.

摘要: 提高农田管理的资源施用效率和持续培育优良作物品种是确保粮食产量和减轻作物生产对环境影响的关 键途径。作物胁迫感知和植物表型测量系统是田间变量管理和高通量植物表型测量研究的核心,且两者在硬件和 数据处理技术上具有相似性。几十年来,人们一直在开发可以用在田间变量管理领域的作物胁迫感知系统,旨在 建立更加可持续的田间管理方案。与此同时,田间高通量表型系统开发取得的重大进展为降低传统表型测量成本 提供了技术基础。本文首先对田间变量管理中涉及的作物胁迫感知系统进行了回顾,特别对目前用于精准灌溉、 氮素施用和农药喷洒中的感知和决策方法进行了总结。基于作者团队在内布拉斯加大学林肯分校开发的三套田间 表型测量系统,对常见田间高通量表型测量系统的传感器和数据的处理分析流程进行了介绍。此外,讨论了当前 田间表型测量系统面临的挑战并提出了潜在解决方案。人工智能、机器人平台和创新仪器的持续发展有望显著提 高测量系统的性能,对系统在育种中的大范围应用起到积极作用。对主要植物生理过程更直接的测量可能成为未 来田间表型研究领域的研究热点之一,并为培育更耐胁迫的作物新品种提供有价值的表型数据。这篇综述可为田 间变量管理和高通量植物表型测量两个研究领域提供参考和独特的见解