text: We present a new vision for smart objects and the Internet of Things wherein mobile robots interact with wirelessly-powered, long-range, ultra-high frequency radio frequency identification (UHF RFID) tags outfitted with sensing capabilities. We explore the technology innovations driving this vision by examining recently-commercialized sensor tags that could be affixed-to or embedded-in objects or the environment to yield true embodied intelligence. Using a pair of autonomous mobile robots outfitted with UHF RFID readers, we explore several potential applications where mobile robots interact with sensor tags to perform tasks such as: soil moisture sensing, remote crop monitoring, infrastructure monitoring, water quality monitoring, and remote sensor deployment.
  @cite_9: This paper reports the results of a 6-month deployment of a 65-node multi-hop network in a vineyard setting. This deployment specifically looked to discover ways in which a farm setting could find a return on investment for deploying such a network. Our ongoing collaborations of over two years ultimately have included everyone from the vineyard owners to the technology developers. We have been able to find several areas where wireless sensor networks deliver valuable information and provide a return on investment.
  @cite_21: The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed.
  @cite_19: article i nfo The remote detection of water stress in a citrus orchard was investigated using leaf-level measurements of chlorophyll fluorescence and Photochemical Reflectance Index (PRI) data, seasonal time-series of crown tem- perature and PRI, and high-resolution airborne imagery. The work was conducted in an orchard where a reg- ulated deficit irrigation (RDI) experiment generated a gradient in water stress levels. Stomatal conductance (Gs) and water potential (Ψ) were measured over the season on each treatment block. The airborne data consisted on thermal and hyperspectral imagery acquired at the time of maximum stress differences among treatments, prior to the re-watering phase, using a miniaturized thermal camera and a micro-hyperspectral imager on board an unmanned aerial vehicle (UAV). The hyperspectral imagery was acquired at 40 cm resolution and 260 spectral bands in the 400-885 nm spectral range at 6.4 nm full width at half maximum (FWHM) spectral resolution and 1.85 nmsampling interval,enablingthe identificationof pure crownsfor extractingradiance andreflectance hyperspectral spectra from each tree. The FluorMOD model was used to investigate the retrieval of chlorophyll fluorescence by applying the Fraunhofer Line Depth (FLD) principle using three spectral bands (FLD3), which demonstrated that fluorescence retrievalwas feasible with the configuration of the UAV micro-hyperspectral in- strument flown over the orchard. Results demonstrated the link between seasonal PRI and crown temperature acquired from instrumented trees and field measurements of stomatal conductance and water potential. The sensitivity of PRI and Tc-Ta time-series to water stress levels demonstrated a time delay of PRI vs Tc-Ta during the recovery phase after re-watering started. At the time of the maximum stress difference among treatment blocks, the airborneimagery acquired fromthe UAV platform demonstrated that the crown temperature yielded the best coefficient of determination for Gs (r 2
  @cite_16: 
  @cite_13: A prototype real-time, smart sensor array for measuring soil moisture and soil temperature that uses off-the-shelf components was developed and evaluated for scheduling irrigation in cotton. The array consists of a centrally located receiver connected to a laptop computer and multiple sensor nodes installed in the field. The sensor nodes consist of sensors (up to three Watermark^(R) soil moisture sensors and up to four thermocouples), a specially designed circuit board, and a Radio Frequency IDentification (RFID) tag which transmits data to the receiver. The smart sensor array described here offers real potential for reliably monitoring spatially variable soil water status in crop fields. The relatively low cost of the system ( USD 2400 for a 20-sensor node system) allows for installation of a dense population of soil moisture sensors that can adequately represent the inherent soil variability present in fields. This paper describes the smart sensor array and testing in a cotton crop. Integration of the sensors with precision irrigation technologies will provide a closed loop irrigation system where inputs from the smart sensor array will determine timing and amounts for real-time site-specific irrigation applications.
  @cite_12: The development of microelectronics began in 1948 and continued with the miniaturisation of sensors during the last ten years. Today, microsystems which are used in silicon microtechnology are called microelectromechanical systems (MEMS). Microsensors are defined as very small sized devices that convert humidity, moisture, temperature, pressure, pollutants, light intensity, etc. into electrical signal. Integration of MEMS with the radio frequency wireless communication technique put forward low power consuming, cheap and communicating small devices called wireless sensor networks (WSN). WSN have spatially distributed transceivers with microsensors that give the possibility of monitoring and collecting data easily and quickly. Nowadays, these devices are used in many different application areas like home automation, forest fire monitoring, traffic control, noise and pollutant detection in the crowded areas. Classical definition of Geographic Information Systems (GIS), which is known by everybody is a computer based system that collects, stores, edits, analyses and displays spatial data. GIS is a powerful tool to access, analyse and evaluate large amounts of spatial data. The total time to get the results is a strategic part of the study. GIS is very efficient for reaching the correct conclusion in a fraction of time. WSN provides huge amount of data. If it would be possible to integrate the power of WSN with GIS, the data can be obtained in a very short time and the evaluation time can be decreased. In this study, integration of WSN with GIS is used for preparation of the soil moisture distribution map of the studied area. Wireless devices with moisture sensed sensors are located at predetermined locations. Geographic coordinates of these points are obtained with GPS. The sensed data is collected by using WSN and evaluated by using GIS. Finally, moisture distribution map of the soil is prepared. We claim that the irrigation of the soil can be done more efficiently by using this prepared map. Moreover, dry situation can be alarmed with this system and adaquate irrigation can be done just on time. In the following section, we briefly introduce wireless sensor networks. In Section 3, and Section 4 we describe the application of WSN for agricultural studies and the integration of WSN and GIS for agricultural fields, respectively. Finally, in Section 5, we conclude the paper with our recommendations.
  @cite_17: This paper presents an overview on recent development of wireless sensor technologies and standards for wireless communications as applied to wireless sensors. Examples of wireless sensors and sensor networks applied in agriculture and food production for environmental monitoring, precision agriculture, M2M-based machine and process control, building and facility automation and RFID-based traceability systems are given. The paper also discusses advantages of wireless sensors and obstacles that prevent their fast adoption. Finally, based on an analysis of market growth, the paper discusses future trend of wireless sensor technology development in agriculture and food industry.
summary: In the context of agricultural sensing, remote crop monitoring (usually via camera imaging) has already proved useful @cite_19 , and it's even feasible to have UAVs obtain direct measurements for applications such as water quality monitoring @cite_16 . Inexpensive sensorized UHF RFID tags have the potential to augment or supplement existing remote-sensing systems to provide precise, direct measurements (eg. for calibration), and could even be used by existing farm equipment to opportunistically obtain relevant agricultural data. Direct sensing can make agricultural systems safer, more efficient, more accurate, and more productive @cite_17 @cite_21 . However, agricultural applications frequently involve large, expansive areas where wiring for communication and power is undesirable or impracticable. Wireless sensor networks have been used collect agriculture data by distributing sensors throughout a field, and transmitting information back to a base station @cite_12 , sometimes using more-advanced mesh networking techniques @cite_9 . These sensor networks have been shown to provide actionable data that can improve growing conditions and irrigation schedules @cite_13 , but they still suffer from battery and cost constraints. UHF RFID sensor tags are a compelling approach to provide direct measurements that enhance other remote sensing techniques.