Thermal Infrared Monitoring for Smart Farming: Agriculture Embracing the IoT
J. M. Andrade, Ph.D.
Department of Electronics, Computing and Mathematics
College of Engineering and Technology – University of Derby (UK)
Earth’s population is forecasted to reach over 9.6 billion people by 2050  which, in turn, implies a 70% increase in food demand . This increase in food production will have an important impact on the amount of water required for irrigation and how efficiently it is used since currently 69% of the world’s fresh water is consumed in agriculture whilst the industrial sector accounts for 19% and the households and municipalities account for 12% of water withdrawals  (see Figure 1 for details on water withdrawals in different world regions). UNESCO in their Managing Water under Uncertainty and Risk report state that the global water demand of agriculture will increase by a further 19% by 2050 . As if the landscape ahead were not promising to be difficult enough, climate change (i.e. alteration of weather patterns and the consequent modification of natural environments) due to the warmth of the Earth's atmosphere, adds more uncertainty to agriculture and water availability. The increasing food demand and water scarcity in the relatively near future require the deployment of innovative smart systems involving cutting-edge technology for data acquisition, data analysis including yield forecasting and resource consumption estimation, and automatic control algorithms to make our food production more efficient and sustainable. In this sense, smart farming (aka precision agriculture) has the potential to improve and enhance agricultural operations towards an efficient use of resources (e.g. land, water, fertilisers, pesticides, energy, labour, time, etc) and maximisation of production and profit through the effective application of technology and data analysis while minimising the agriculture’s impact on the environment.
In order to use the correct amount of water needed by crops, the monitoring of plant stress (e.g. hydric stress) along with the measurement of soil characteristics (e.g. soil moisture, pH, soil nitrate, soil temperature to name a few) and weather conditions (e.g. air temperature, humidity, wind speed and incident radiation) are needed within the irrigation control system and crop management. Thus, data on water usage and crop health may be considered for decision making in farming and food business operations. In this regards, leaf temperature may provide relevant physiological information such as transpiration, hydric stress and disease conditions that can be used for both monitoring and control purposes (see Figure 2). By monitoring the leaf temperature along with other soil and weather variables, the irrigation system is expected to be more efficient since only the needed amount of water may be regulated by an automatic control system. Furthermore, the impact of diseases on agricultural production can be controlled and hence reduced if the health condition of crops is monitored appropriately and the corresponding actions are taken on time. This can be translated into an expected reduction of the use of pesticides.
The main barriers for not adopting smart farming are (1) the initial cost of the technology required and (2) the uncertainty associated with the prediction of return on investment due to the market fluctuation of produce prices and cost of fuel and energy in general, fertilisers, water, etc. The costs of technology have decreased and low cost development electronic boards are likely to help to increase the adoption of smart farming. In this respect, the Grid-EYE development kit, which at the time of writing this note may be acquired from Farnell Element 14 at a cost of £48.54 (i.e. about US$ 60.00) illustrates the availability of cutting-edge technology at a relatively low cost. Nevertheless, the cost of sensors for agricultural applications is still relatively high and even more considering the large number of sensors that may be required in a traditional static sensor network for extensive fields. Smart systems involving unmanned ground vehicles (UGV) and/or unmanned aerial vehicle (UAV) seem to be the right solution since these may be used to deploy dynamic sensor nodes that track predefined trajectories using Global Information Systems (GIS) and Global Positioning Systems (GPS), and execute pre-established schedules for data gathering alongside pre-processing and data transmission. The use of UGV/UAV offers more functionality as it can be used in different fields, even remotely located, maximising the return on investment. Thus, the number of sensor nodes may be reduced dramatically.
The Grid-EYE IR array sensor evaluation kit integrates the Panasonic’s state of the art Grid-EYE sensor (see Figure 3), Panasonic nanopower PAN1740 Bluetooth smart module (a short-range Bluetooth low energy (BLE) single mode compliant with Bluetooth v 4.0 standard) and the ATSAMD21G18 smart ARM-based microcontroller (a low-power microcontroller using the 32-bit ARM Cortex-M0+ processor) on a single PCB as shown in Figure 3 . This electronic board allows for two modes of operation: (1) standalone with a USB interface for PC connection and Bluetooth interface for communication with a Smartphone, and (2) Arduino mode in which the sampled data by the Grid-EYE sensor is transmitted to the Arduino (DUE) mother board through the I2C interface and then the data may be transmitted to a PC via USB or a Smartphone via Bluetooth .
Figure 3: Panasonic Grid-EYE AMG8832EK Evaluation Board (photo by Dr JM Andrade).
The Grid-EYE infrared (IR) array sensor deployed by Panasonic is the first-ever surface mount thermopile array sensor. This device features 64 thermopile elements in an 8x8 grid format (see Figure 4) that detect absolute temperatures by infrared radiation. The Grid-EYE sensor is able to measure actual temperature and temperature gradients, thus providing thermal images for a wide range of applications (e.g. energy saving in commercial, public and residential places; thermal imaging, patient detection, and positioning and movement detection for the medical industry; deployment of security systems for lifts and ATMs among others; industrial temperature measurement; household applications such as air conditioning, heating systems, cooking and microwave stoves).
Infrared thermal sensing allows for measuring and mapping surface temperature and thermal distribution passively and non-intrusively which makes it convenient for leaf temperature monitoring and other agricultural applications. The GRID-EYE IR sensor is an innovative 8´8 thermopile array (see Figure 4) based on Panasonic’s micro-electromechanical system (MEMS) technology that combines the sensor chip, a digital ASIC and a silicon lens . The 64 thermopile elements of the Grid-EYE sensor detect absolute surface temperature in a contactless manner. This start-of-the-art sensor offers motionless object detection, movement direction and a thermal image with 64 pixel resolution . Thermal imaging may be used as a diagnostic tool in agriculture (e.g. leaf temperature monitoring to establish crop health) in a similar way as it is used for monitoring electrical equipment and/or installations. An abnormal thermal distribution can be an indication of hydric stress and/or a disease condition in plants (see Figure 2) whose opportune treatment is expected to have a positive impact on agriculture. The Grid-EYE IR sensor can also be integrated into the irrigation control system which will have a favourable effect on water consumption. Moreover, the sampled data temperature may be used for other monitoring purposes and hence effective decision making within the food production industry.
Dr JM Andrade, a control systems R&D engineer and senior lecturer in electrical and electronic engineering from the Electronics, Computing and Mathematics Department of the University of Derby (UK), has been sponsored by Farnell Element 14, the leading distributor of electronic components, in collaboration with Panasonic, the Japanese electronics manufacturer, with Grid-EYE evaluation kits to be used in a research project on thermal infrared monitoring for smart farming. Only a few engineering schools from British universities have been invited to participate in the Grid-EYE Lab Test. This programme allows academics to integrate cutting-edge technology into their activities by providing them with evaluation boards and tools to carry out research projects to develop their ideas. Dr Andrade was delighted with the invitation made by Farnell Element 14 at the end of October 2016. Figure 5 shows a photograph of the Grid-EYE evaluation kits already received by Dr Andrade for his research project. Application of different monitoring, estimation and control algorithms will be explored within this smart farming project in order to illustrate the integration of the IoT in agriculture as a means to overcome problems of capital relevance as described at the beginning of this article. In particular, the Panasonic Grid-EYE kits consisting of hardware and software tools provided by Farnell Element 14 in collaboration with Panasonic will be key for the project. Dr Andrade has already invited some undergraduate students from the University of Derby and is also expecting two students from the Polytech Clermont-Ferrand in France to work alongside him on this fascinating R&D project. The students will largely benefit from the hands-on engineering experience that they will acquire by working on this project, which will follow an industry-driven R&D ethos led by Dr Andrade who has worked as an R&D engineer himself.
List of References:
 United Nations (2012) World Population Prospect: the 2012 Revision.
 WWAP: World Water Assessment Programme (2012) The United Nations World Water Development Report 4: Managing Water under Uncertainty and Risk. Paris, UNESCO.
 AQUASTAT (2014) Water withdrawal by sector (agriculture, industry and households).
 IAASTD: International Assessment of Agricultural Knowledge, Science and Technology for Development (2016) Agriculture at a Crossroads – IAASTD Findings and Recommendations for Future Farming.
 Ortiz, B. Shaw, J. and Fulton, J. (2011) Basic of Crop Sensing. Alabama Cooperative Extension System. ANR-1398.
 Panasonic (2016) Grid-EYE Evaluation Kit: User Manual.
 Panasonic (2016) Grid-EYE: State of the Art Thermal Imaging Solution – Smallest Size Solution for Infrared Detection. White Paper v 1.0.