Development of a Sensor Node for Precision Horticulture

This paper presents the design of a new wireless sensor node (GAIA Soil-Mote) for precision horticulture applications which permits the use of precision agricultural instruments based on the SDI-12 standard. Wireless communication is achieved with a transceiver compliant with the IEEE 802.15.4 standard. The GAIA Soil-Mote software implementation is based on TinyOS. A two-phase methodology was devised to validate the design of this sensor node. The first phase consisted of laboratory validation of the proposed hardware and software solution, including a study on power consumption and autonomy. The second phase consisted of implementing a monitoring application in a real broccoli (Brassica oleracea L. var Marathon) crop in Campo de Cartagena in south-east Spain. In this way the sensor node was validated in real operating conditions. This type of application was chosen because there is a large potential market for it in the farming sector, especially for the development of precision agriculture applications

Decision support for optimised irrigation scheduling

The system, developed under the FLOW-AID (an FP6 project), is a farm level water management system of special value in situations where the water availability and quality is limited. This market-ready precision irrigation management system features new models, hardware and software. The hardware platform delivers a maintenance-free low cost dielectric tensiometer and several low-end irrigation or fertigation controllers for serving different situations. The software includes a complete, web based, Decision Support System (DSS) that consists of an expert planner for farm zoning (MOPECO) and a universal irrigation scheduler, based on crop-water stress models (UNIPI) and water and nutrient uptake calculations. The system, designed also to service greenhouse fertigation and hydroponics, is scalable from one to many zones. It consists of 1) a data gathering tool which uploads agronomic data, from monitored crops around the world, to a central web Data Base (DB), and 2) a web based Decision Support System (DSS). The DSS processes intelligently the data of the crop using Crop Response Models, Nutrient Uptake Models and Water Uptake Models. The central system returns over Internet to the low-end controller a command file containing water scheduling and nutrient supply guideline

Farm level optimal water management: Assistant for irrigation under Defecit (FLOW-AID)

Flow-aid is an on-going 6th Framework European project (2006-2009) with the objective to contribute to sustainable irrigated agriculture by developing an irrigation management system that can be used for crop production in cases with limited water supply and marginal water quality. The project integrates innovative sensor technologies into a decision support system, taking into consideration boundary conditions and constraints for a number of practical growing systems in the Mediterranean. It focuses on innovative, simple and affordable, hard- and software concepts for deficit irrigation; particularly a maintenance free tensiometer, a wireless and low-power sensor network; an expert system to assist annual farm zoning and crop planning in view of expected water availability and quality; and an irrigation scheduler for allocation of water for multiple plots at farm level. The system is being evaluated at four sites located in Italy, Turkey, Lebanon and Jordan. The sites are chosen in such a way that they differ in the type of constraints, irrigation structures, crop types, water supplies (availability of amount and quality), the local goals, and their complexity. This paper describes the overall concept and briefly the progress of the first year research

IoT-based systems for soil nutrients assessment in horticulture

Soil nutrients assessment has great importance in horticulture. Implementation of an information system for horticulture faces many challenges: (i) great spatial variability within farms (e.g., hilly topography); (ii) different soil properties (e.g., different water holding capacity, different content in sand, sit, clay, and soil organic matter, different pH, and different permeability) for different cultivated plants; (iii) different soil nutrient uptake by different cultivated plants; (iv) small size of monoculture; and (v) great variety of farm components, agroecological zone, and socio-economic factors. Advances in information and communication technologies enable creation of low cost, efficient information systems that would improve resources management and increase productivity and sustainability of horticultural farms. We present an information system based on different sensing capability, Internet of Things, and mobile application for horticultural farms. An overview on different techniques and technologies for soil fertility evaluation is also presented. The results obtained in a botanical garden that simulates the diversity of environment and plant diversity of a horticultural farm are discussed considering the challenges identified in the literature and field research. The study provides a theoretical basis and technical support for the development of technologies that enable horticultural farmers to improve resources management.info:eu-repo/semantics/publishedVersio

Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming

[EN] Improving the sustainability in agriculture is nowadays an important challenge. The automation of irrigation processes via low-cost sensors can to spread technological advances in a sector very influenced by economical costs. This article presents an auto-calibrated pH sensor able to detect and adjust the imbalances in the pH levels of the nutrient solution used in hydroponic agriculture. The sensor is composed by a pH probe and a set of micropumps that sequentially pour the different liquid solutions to maintain the sensor calibration and the water samples from the channels that contain the nutrient solution. To implement our architecture, we use an auto-calibrated pH sensor connected to a wireless node. Several nodes compose our wireless sensor networks (WSN) to control our greenhouse. The sensors periodically measure the pH level of each hydroponic support and send the information to a data base (DB) which stores and analyzes the data to warn farmers about the measures. The data can then be accessed through a user-friendly, web-based interface that can be accessed through the Internet by using desktop or mobile devices. This paper also shows the design and test bench for both the auto-calibrated pH sensor and the wireless network to check their correct operation.The research leading to these results has received funding from "la Caixa" Foundation and Triptolemos Foundation. This work has also been partially supported by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIRCambra-Baseca, C.; Sendra, S.; Lloret, J.; Lacuesta, R. (2018). Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming. Sensors. 18(5):1-16. https://doi.org/10.3390/s18051333S116185Salley, S. W., Sleezer, R. O., Bergstrom, R. M., Martin, P. H., & Kelly, E. F. (2016). A long-term analysis of the historical dry boundary for the Great Plains of North America: Implications of climatic variability and climatic change on temporal and spatial patterns in soil moisture. Geoderma, 274, 104-113. doi:10.1016/j.geoderma.2016.03.020Yang, H., Du, T., Qiu, R., Chen, J., Wang, F., Li, Y., … Kang, S. (2017). Improved water use efficiency and fruit quality of greenhouse crops under regulated deficit irrigation in northwest China. Agricultural Water Management, 179, 193-204. doi:10.1016/j.agwat.2016.05.029Ferentinos, K. P., Katsoulas, N., Tzounis, A., Bartzanas, T., & Kittas, C. (2017). Wireless sensor networks for greenhouse climate and plant condition assessment. Biosystems Engineering, 153, 70-81. doi:10.1016/j.biosystemseng.2016.11.005Ibayashi, H., Kaneda, Y., Imahara, J., Oishi, N., Kuroda, M., & Mineno, H. (2016). A Reliable Wireless Control System for Tomato Hydroponics. Sensors, 16(5), 644. doi:10.3390/s16050644Ntinas, G. K., Neumair, M., Tsadilas, C. D., & Meyer, J. (2017). Carbon footprint and cumulative energy demand of greenhouse and open-field tomato cultivation systems under Southern and Central European climatic conditions. Journal of Cleaner Production, 142, 3617-3626. doi:10.1016/j.jclepro.2016.10.106Europapress Newshttp://www.europapress.es/andalucia/almeria-00350/noticia-superficie-invernaderos-crece-105-ultimos-cuatro-anos-llegar-29596-hectareas-20150213102204.htmlTreftz, C., & Omaye, S. T. (2016). Hydroponics: potential for augmenting sustainable food production in non-arable regions. Nutrition & Food Science, 46(5), 672-684. doi:10.1108/nfs-10-2015-0118De Anda, J., & Shear, H. (2017). Potential of Vertical Hydroponic Agriculture in Mexico. Sustainability, 9(1), 140. doi:10.3390/su9010140Croft, M. M., Hallett, S. G., & Marshall, M. I. (2017). Hydroponic production of vegetable Amaranth (Amaranthus cruentus) for improving nutritional security and economic viability in Kenya. Renewable Agriculture and Food Systems, 32(6), 552-561. doi:10.1017/s1742170516000478Ferrarezi, R. S., & Testezlaf, R. (2014). Performance of wick irrigation system using self-compensating troughs with substrates for lettuce production. Journal of Plant Nutrition, 39(1), 147-161. doi:10.1080/01904167.2014.983127Understanding Irrigation Water Test Results and Their Implications on Nursery and Greenhouse Crophttps://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1160&context=anr_reportsKim, H.-J., Kim, D.-W., Kim, W. K., Cho, W.-J., & Kang, C. I. (2017). PVC membrane-based portable ion analyzer for hydroponic and water monitoring. Computers and Electronics in Agriculture, 140, 374-385. doi:10.1016/j.compag.2017.06.015(2017). Remote Sensing for Irrigation of Horticultural Crops. Horticulturae, 3(2), 40. doi:10.3390/horticulturae3020040Suárez-Albela, M., Fraga-Lamas, P., Fernández-Caramés, T., Dapena, A., & González-López, M. (2016). Home Automation System Based on Intelligent Transducer Enablers. Sensors, 16(10), 1595. doi:10.3390/s16101595Zhang, Q., Yang, X., Zhou, Y., Wang, L., & Guo, X. (2007). A wireless solution for greenhouse monitoring and control system based on ZigBee technology. Journal of Zhejiang University-SCIENCE A, 8(10), 1584-1587. doi:10.1631/jzus.2007.a1584Gill, S. S., Chana, I., & Buyya, R. (2017). IoT Based Agriculture as a Cloud and Big Data Service. Journal of Organizational and End User Computing, 29(4), 1-23. doi:10.4018/joeuc.2017100101Nordic Semiconductor, RF Specialist in Ultra-Low Power Wireless Communicationshttp://www.nordicsemi.com/eng/Products/2.4GHzRF/nRF24L01Pawlowski, A., Guzman, J., Rodríguez, F., Berenguel, M., Sánchez, J., & Dormido, S. (2009). Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control. Sensors, 9(1), 232-252. doi:10.3390/s90100232Li, X., Cheng, X., Yan, K., & Gong, P. (2010). A Monitoring System for Vegetable Greenhouses based on a Wireless Sensor Network. Sensors, 10(10), 8963-8980. doi:10.3390/s10100896

Opportunities for improving irrigation efficiency with quantitative models, soil water sensors and wireless technology

Increasingly serious shortages of water make it imperative to improve the efficiency of irrigation in agriculture, horticulture and in the maintenance of urban landscapes. The main aim of the current review is to identify ways of meeting this objective. After reviewing current irrigation practices, discussion is centred on the sensitivity of crops to water deficit, the finding that growth of many crops is unaffected by considerable lowering of soil water content and, on this basis, the creation of improved means of irrigation scheduling. Subsequently, attention is focused on irrigation problems associated with spatial variability in soil water and the often slow infiltration of water into soil, especially the subsoil. As monitoring of soil water is important for estimating irrigation requirements, the attributes of the two main types of soil water sensors and their most appropriate uses are described. Attention is also drawn to the contribution of wireless technology to the transmission of sensor outputs. Rapid progress is being made in transmitting sensor data, obtained from different depths down the soil profile across irrigated areas, to a PC that processes the data and on this basis automatically commands irrigation equipment to deliver amounts of water, according to need, across the field. To help interpret sensor outputs, and for many other reasons, principles of water processes in the soil–plant system are incorporated into simulation models that are calibrated and tested in field experiments. Finally, it is emphasized that the relative importance of the factors discussed in this review to any particular situation varies enormously

Wireless Sensor Technologies and Applications

Recent years have witnessed tremendous advances in the design and applications of wirelessly networked and embedded sensors. Wireless sensor nodes are typically low-cost, low-power, small devices equipped with limited sensing, data processing and wireless communication capabilities, as well as power supplies. They leverage the concept of wireless sensor networks (WSNs), in which a large (possibly huge) number of collaborative sensor nodes could be deployed. As an outcome of the convergence of micro-electro-mechanical systems (MEMS) technology, wireless communications, and digital electronics, WSNs represent a significant improvement over traditional sensors. In fact, the rapid evolution of WSN technology has accelerated the development and deployment of various novel types of wireless sensors, e.g., multimedia sensors. Fulfilling Moore’s law, wireless sensors are becoming smaller and cheaper, and at the same time more powerful and ubiquitous. [...

Automation and Robotics Used in Hydroponic System

Hydroponic system requires periodic labor, a systematic approach, repetitive motion and a structured environment. Automation, robotics and IoT have allowed farmers to monitoring all the variables in plant, root zone and environment under hydroponics. This research introduces findings in design with real time operating systems based on microcontrollers; pH fuzzy logic control system for nutrient solution in embed and flow hydroponic culture; hydroponic system in combination with automated drip irrigation; expert system-based automation system; automated hydroponics nutrition plants systems; hydroponic management and monitoring system for an intelligent hydroponic system using internet of things and web technology; neural network-based fault detection in hydroponics; additional technologies implemented in hydroponic systems and robotics in hydroponic systems. The above advances will improve the efficiency of hydroponics to increase the quality and quantity of the produce and pose an opportunity for the growth of the hydroponics market in near future