A comprehensive review of proximal spectral sensing devices and diagnostic equipment for field crop growth monitoring

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2025-05-25 DOI:10.1007/s11119-025-10251-3

Yongxian Wang, Jingwei An, Mingchao Shao, Jianshuang Wu, Dong Zhou, Xia Yao, Xiaohu Zhang, Weixing Cao, Chongya Jiang, Yan Zhu

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引用次数: 0

Abstract

Purpose

This review synthesizes advancements in proximal spectral sensing devices—including portable, vehicle-based, UAV-based, and IoT-based—for monitoring field crop growth traits. By evaluating their technical capabilities, applications, and limitations, it addresses critical challenges in scalability, data integration, and environmental adaptability to advance precision agriculture (PA) practices.

Methods

A systematic analysis of literature (2001–2024) was conducted using keywords such as “proximal remote sensing,” “spectral sensors,” and “crop growth monitoring” in the Web of Science database, yielding 1,278 publications. The performance, sensing mechanisms, and practical applications of these devices were analyzed across platforms, with a focus on their ability to estimate key growth indicators (e.g., biomass, leaf area index, nitrogen content) and resolve PA-related challenges.

Results

Portable spectral sensors excel in capturing high-resolution, targeted measurements but face limitations in accuracy during early crop growth stages and under complex field conditions. Vehicle-based systems enable efficient large-area scanning but encounter synchronization challenges between sensors and machinery, alongside susceptibility to environmental interference. UAV-based devices deliver rapid, high-throughput data collection but require enhanced endurance and integration with satellite imagery to achieve regional scalability. IoT-based networks support continuous monitoring but are constrained by a lack of specialized spectral sensors and insufficient durability in harsh agricultural environments. Cross-platform data fusion remains impeded by heterogeneity in data types, spatial scales, and storage protocols, while device durability, algorithmic robustness, and environmental resilience emerge as critical factors for reliable field deployment.

Conclusions

Proximal spectral sensing devices hold transformative potential for multi-scale crop growth monitoring, yet persistent technical gaps hinder their widespread adoption. Future research should prioritize the development of lightweight hyperspectral imaging systems paired with advanced computational algorithms, unified frameworks for cross-platform data fusion, and durable IoT sensors tailored for harsh field conditions. Additionally, integrating UAV-based data with satellite observations will enhance regional insights, while standardized protocols and interdisciplinary collaboration are essential to bridge ground-to-space monitoring networks. These advancements will foster intelligent, sustainable crop management systems, ultimately addressing global agricultural productivity and sustainability challenges.

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近端光谱传感装置和田间作物生长监测诊断设备综述

目的综述了近端光谱传感设备的研究进展，包括便携式、车载、无人机和物联网等。通过评估它们的技术能力、应用和局限性，它解决了可扩展性、数据集成和环境适应性方面的关键挑战，以推进精准农业（PA）实践。方法利用Web of Science数据库中“近端遥感”、“光谱传感器”、“作物生长监测”等关键词对2001-2024年的文献进行系统分析，共收录文献1278篇。研究人员跨平台分析了这些设备的性能、传感机制和实际应用，重点关注了它们估算关键生长指标（如生物量、叶面积指数、氮含量）和解决pa相关挑战的能力。结果便携式光谱传感器在捕获高分辨率、有针对性的测量方面具有优势，但在作物早期生长阶段和复杂的田间条件下，其精度存在局限性。基于车辆的系统能够实现高效的大面积扫描，但会遇到传感器和机械之间的同步挑战，以及对环境干扰的敏感性。基于无人机的设备提供快速、高通量的数据收集，但需要增强耐用性，并与卫星图像集成，以实现区域可扩展性。基于物联网的网络支持持续监测，但受到缺乏专业光谱传感器和恶劣农业环境耐久性不足的限制。跨平台数据融合仍然受到数据类型、空间尺度和存储协议异质性的阻碍，而设备耐用性、算法鲁棒性和环境弹性成为可靠现场部署的关键因素。结论近端光谱传感装置在多尺度作物生长监测中具有变革性的潜力，但持续的技术差距阻碍了其广泛应用。未来的研究应优先发展轻型高光谱成像系统，与先进的计算算法、跨平台数据融合的统一框架以及为恶劣现场条件量身定制的耐用物联网传感器相结合。此外，将基于无人机的数据与卫星观测相结合将增强区域洞察力，而标准化协议和跨学科合作对于连接地对空监测网络至关重要。这些进步将促进智能、可持续的作物管理系统，最终解决全球农业生产力和可持续性挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.