浅析农业生产传感器的应用及发展

传感器技术在现代农业生产中可谓无处不在,文章重点探究了光电传感器、称重传感器和无线传感器在农业生产中的应用实例,在此基础上从微型化、智能化和多功能化三方面展望了农业生产传感器的发展趋势,以期为相关的研究提供理论参考。     

农业传感器技术在我国的应用和市场：现状与未来展望

农业传感器技术是农业信息化的基础，是实现农业现代化的核心要素和关键支撑之一。首先，本文在总结农业传感器技术在智能农机装备、农用无人机遥感及农业物联网三方面的研究及应用现状的基础上，对我国农业传感器技术需求和市场发展进行了深入分析。其次，通过技术产业调研分析，对农业传感器产业化、市场化及未来的发展趋势进行了总结与展望。最后，凝练了农业传感器产业领域的16项关键技术，并在此基础上开展了德尔菲法专家问卷调查，阐明了农业传感器最重要的属性是通用性，明确了相关技术发展最大的制约因素是基础理论和研发投入，提出了农业传感器技术将朝着低成本化、高稳定性、高智能化、可移植性、可操作性方向发展。本文可为我国农业传感器技术研发和产业发展提供参考。     

传感器在智慧农业中的应用分析

本文介绍了国内智慧农业的发展现状,分析了传感器在智慧农业中的应用,具体包括农业生产、监测管理和加工储存等方面,最后对传感器的未来发展进行了分析和展望,以期为智慧农业的发展提供参考。     

无线传感器网络技术在精细农业中的应用进展

综述无线传感器网络技术在节点构成、网络拓扑、通信协议等方面的特点,重点介绍无线传感器网络技术在精细农业中的典型应用,认为无线传感器网络技术应用于精细农业,需解决信号传输与衰减方式建模、多通信网融合及降低传感器成本等关键问题.提出将无线传感器网络技术与农艺技术、农业机械化及自动化技术等相结合,有助于优化农业决策支持系统,...     

浅谈传感器在设施农业中的应用

随着传感器技术和农业现代化的发展,传感器在设施农业中的应用越来越广。分析了设施农业中传感器的重要性,并阐述了设施农业中传感器的种类及其在设施农业中的应用。     

基于无线传感器网络的农业精量灌溉系统设计

阐述了我国农业领域用水资源紧缺的现状和实施精量灌溉的意义,从无线传感器网络的系统结构、路由协议、成本控制等方面设计了基于无线传感器网络的农业精量灌溉系统,对实现无线传感器网络技术和农业灌溉技术相结合,达到精量灌溉的目标,进而提高水资源利用率,缓解水资源紧张问题进行了探索。     

传感器在现代农业中的应用

现代农业的发展离不开各种传感器的应用,本文从种植、饲养、水产、储藏与加工等方面分析传感器在现代农业中的应用和发展,重点分析了传感器代替农民的人工经验后在成本和农业产出上的巨大优势,以期为传感器在现代农业中的进一步推广、使用和发展提供参考。     

无线传感器网络在农业中的应用研究现状与展望

无线传感器网络结合了传感器、微处理器和无线通信接口,是一门日益引起人们研究兴趣的技术.作者结合农业的实际特点,综述了无线传感器网络在农业中的研究现状及涉及的关键技术,最后分析了无限传感器网络在农业中的应用前景,为无线传感器网络在农业中的深入应用拓宽思路.     

数字农业现状与发展探析——以数字化温室为例

本文以数字农业技术在数字化温室中的应用为例,通过调研数字农业设备、技术和人才方面的现状,分析了国内目前农业传感器技术、通信技术、农业模型技术、农业智能设备技术、采后管理技术、农业数字化人才等方面存在的问题,提出了一些解决措施,以期为从事数字农业的职业新农民提供参考。     

传感器在智慧农业上的应用分析

本文指出了国内智慧农业的发展现状和充分肯定传感器在智慧农业上的重要作用,分析了传感器在智慧农业中农业生产、监测管理和加工储存等具体应用领域,最后对传感器的未来发展进行了分析和展望。     

High resolution satellite imaging sensors for precision agriculture

The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980s. Various types of remote sensors carried on ground-based platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications, including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.     

传感器在精准农业中的应用

加快我国精准农业发展速度,拓宽其普及应用范围,发展高效、稳定、价格低廉的传感器至关重要。介绍 了生物传感器、电子舌、电子鼻、机器视觉等新型传感器的基本原理及其在农业领域的应用现状,并探讨了在农副产 品生产、加工、储运和流通的各个环节对传感器的不同需求,同时对农情监控传感器的发展方向进行展望。     

计算机技术在智慧农业中的应用初探

互联网和人工智能技术的发展,加快农业这一人类历史上最古老职业的信息化发展。 智慧农业以智能化、数据化、精准化、集约化为发展目标,依托于传感器、物联网、云计算和大数 据,提高农作物的生产效率,提高我国农民的收入,推动我国智慧农业发展的进程,为我国的智 慧农业发展提供良好的、适宜生长的环境。     

无线传感器网络在设施农业中的应用进展

探索无线传感器网络在设施农业中的应用,研制基于无线传感器网络的设施农业环境监控系统,已经成为研究热点。提出了基于无线传感器网络的设施农业环境监控系统的基本结构,对国内外相关典型应用成果进行了评述,对目前存在的主要问题进行了剖析,同时提出了未来的努力方向。目前,基于无线传感器网络的设施农业环境监控系统还存在诸多问题,包括感知网络的功能性问题、传感器的即插即用问题、无线传感器网络与广域网的互连问题、上位机软件的功能性问题、传感器节点的功耗问题、监控系统的安全性问题等。未来需要解决这些问题,以进一步提高基于无线传感器网络的设施农业环境监控系统的自动化、智能化程度,使之满足设施农业应用的实际需要。图2表1参33     

Sensing technologies for precision specialty crop production

With the advances in electronic and information technologies, various sensing systems have been developed for specialty crop production around the world. Accurate information concerning the spatial variability within fields is very important for precision farming of specialty crops. However, this variability is affected by a variety of factors, including crop yield, soil properties and nutrients, crop nutrients, crop canopy volume and biomass, water content, and pest conditions (disease, weeds, and insects). These factors can be measured using diverse types of sensors and instruments such as field-based electronic sensors, spectroradiometers, machine vision, airborne multispectral and hyperspectral remote sensing, satellite imagery, thermal imaging, RFID, and machine olfaction system, among others. Sensing techniques for crop biomass detection, weed detection, soil properties and nutrients are most advanced and can provide the data required for site specific management. On the other hand, sensing techniques for diseases detection and characterization, as well as crop water status, are based on more complex interaction between plant and sensor, making them more difficult to implement in the field scale and more complex to interpret. This paper presents a review of these sensing technologies and discusses how they are used for precision agriculture and crop management, especially for specialty crops. Some of the challenges and considerations on the use of these sensors and technologies for specialty crop production are also discussed.     

Sensor data fusion to predict multiple soil properties

The accuracy of a single sensor is often low because all proximal soil sensors respond to more than one soil property of interest. Sensor data fusion can potentially overcome this inability of a single sensor and can best extract useful and complementary information from multiple sensors or sources. In this study, a data fusion was performed of a Vis?CNIR spectrometer and an EM38 sensor for multiple soil properties. Stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and principal components analysis combined with stepwise multiple linear regression (PCA?+?SMLR) methods were used in three different fields. Soil properties investigated for data fusion included soil texture (clay, silt and sand), EC, pH, total organic carbon (TOC), total nitrogen (TN) and carbon to nitrogen ratio (CN). It was found that soil property models based on fusion methods significantly improved the accuracy of predictions of soil properties measureable by both sensors, such as clay, silt, sand, EC and pH from those based on either of the individual sensors. The accuracy of predictions of TOC, TN and CN was also improved in some cases, but was not consistent in all fields. Among data fusion methods, PLSR outperformed both SMLR and PCA?+?SMLR methods because it proved to have a better ability to deal with the multi-collinearity among the predictor variables of both sensors. The best data fusion results were found in a clayey field and the worst in a sandy field. It is concluded that sensor data fusion can enhance the quality of soil sensing in precision agriculture once a proper set of sensors has been selected for fusion to estimate desired soil properties. More efficient statistical data analysis methods are needed to handle a large volume of data effectively from multiple sensors for sensor data fusion.     

Participative site-specific agriculture analysis for smallholders

Site-specific agriculture has been adopted in a high-tech context using, for instance, in situ sensors, satellite images for remote sensing analysis, and some other technological devices. However, farmers and smallholders without the economic resources and required knowledge to use and to access the latest technology seem to find an impediment to precision agricultural practices. This article discusses the possibility of adopting precision agriculture (PA) principles for site-specific management but in a low technology context for such farmers. The proposed methodology to support PA combines low technology dependency and a participatory approach by involving smallholders, farmers and experts. The case studies demonstrate how the interplay of low technology and a participative approach may be suitable for smallholders for site-specific agriculture analysis.     

Agricultural applications of a low-cost infrared thermometer

Plant canopy temperature is used in many studies of plant/environment interactions and non-contact measurement is often made with radiometric surface thermometers commonly referred to as infrared thermometers. Industrial-quality infrared thermocouples are widely available and often used in agricultural research. While research on canopy temperature has provided management tools for production agriculture, the high cost of the industrial-quality infrared thermocouples has limited their adoption and use in production agriculture settings. Our objective was to evaluate a low-cost consumer-quality infrared thermocouple as a component of a wireless thermal monitoring system designed for use in a production agriculture setting. The performances of industrial-quality and low-cost consumer-quality sensors were compared under controlled constant temperature and under field conditions using both grass and cotton canopies. Results demonstrate that under controlled constant-temperature the two types of infrared thermocouples were “significantly the same” at 10 °C, 20 °C and 30 °C and “significantly not the same” at 40 °C and 50 °C. Across the temperature range tested, the consumer-quality infrared thermocouples temperature reading was closer to the thermocouple reading than the industrial-quality infrared thermocouples. A field comparison of industrial-quality and consumer-quality infrared thermocouple sensors monitoring a grass canopy and a cotton canopy indicated that the two types of sensors were similar over a 13–35 °C range. The measurement of temperature made with two types of sensors would not differ significantly. Based on these results we conclude that the lower-cost consumer-quality infrared thermometers are suitable for use in production agricultural applications.