A Monitoring System for Vegetable Greenhouses based on a Wireless Sensor Network

A wireless sensor network-based automatic monitoring system is designed for monitoring the life conditions of greenhouse vegetatables. The complete system architecture includes a group of sensor nodes, a base station, and an internet data center. For the design of wireless sensor node, the JN5139 micro-processor is adopted as the core component and the Zigbee protocol is used for wireless communication between nodes. With an ARM7 microprocessor and embedded ZKOS operating system, a proprietary gateway node is developed to achieve data influx, screen display, system configuration and GPRS based remote data forwarding. Through a Client/Server mode the management software for remote data center achieves real-time data distribution and time-series analysis. Besides, a GSM-short-message-based interface is developed for sending real-time environmental measurements, and for alarming when a measurement is beyond some pre-defined threshold. The whole system has been tested for over one year and satisfactory results have been observed, which indicate that this system is very useful for greenhouse environment monitoring

Root Zone Sensors for Irrigation Management in Intensive Agriculture

Crop irrigation uses more than 70% of the world’s water, and thus, improving irrigation efficiency is decisive to sustain the food demand from a fast-growing world population. This objective may be accomplished by cultivating more water-efficient crop species and/or through the application of efficient irrigation systems, which includes the implementation of a suitable method for precise scheduling. At the farm level, irrigation is generally scheduled based on the grower’s experience or on the determination of soil water balance (weather-based method). An alternative approach entails the measurement of soil water status. Expensive and sophisticated root zone sensors (RZS), such as neutron probes, are available for the use of soil and plant scientists, while cheap and practical devices are needed for irrigation management in commercial crops. The paper illustrates the main features of RZS’ (for both soil moisture and salinity) marketed for the irrigation industry and discusses how such sensors may be integrated in a wireless network for computer-controlled irrigation and used for innovative irrigation strategies, such as deficit or dual-water irrigation. The paper also consider the main results of recent or current research works conducted by the authors in Tuscany (Italy) on the irrigation management of container-grown ornamental plants, which is an important agricultural sector in Italy

RASPBRRY PI Based Greenhouse Management System

In an trade throughout bound hazards is are going to be terribly tough to observe the parameter through wires and analog devices like transducers .The greenhouse vegetable production desires less labor, less capital, has quicker returns than traditional vegetable production. And it can't be simply influenced by the climate. thus the greenhouse vegetables are wanted by vegetable growers. it's terribly tough to manage scattered greenhouse while not a far off surroundings observation system. This project uses sensing elements such Temperature sensor (LM35), LDR. The temperature sensing element LM35 senses the temperature and converts it into an electrical (analog) signal, that is applied to the small controller through ADC. The analog signal is regenerate into digital format by the analog-digital converter (ADC). because the explicit temperature device is activated high, the load (Fan) is ON. within the same approach the LDR senses night, the load (bulb) are going to be ON. Here two temperature sensors and two LDR sensors are used. Raspberry pi is that the heart of the whole system. The Raspberry Pi could be a credit-card-sized single-board pc developed within the UK by the Raspberry Pi Foundation . The Raspberry Pi incorporates a Broadcom BCM2835 system on a chip which has an ARM1176JZF 700 rate processor Video Core IV GPU and was originally shipped with 256 megabytes of RAM, later Upgraded to 512 MB. It doesn't embody a constitutional fixed disk or solid-state drive, however Uses an SD card for booting and long storage

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

Development of a remote sensing and control system for greenhouse applications

Real-time monitoring provides reliable, timely information of crop and soil status, important in taking decisions for crop production improvement. This work presents a real-time monitoring and control system for climatological variables in greenhouse. The system has wireless communication capabilities, which allow it to cover extensive surfaces in real-time, without extra resources. The system implementation is based on the micro controllers “PIC18F4550” and “DSPIC 30F5011”, user interface was programmed under LINUX. The proposed system performance was compared with commercial Data Loggers, readings present a linear adjustment with R2=0.9656

Development of a remote sensing and control system for greenhouse applications

Real-time monitoring provides reliable, timely information of crop and soil status, important in taking decisions for crop production improvement. This work presents a real-time monitoring and control system for climatological variables in greenhouse. The system has wireless communication capabilities, which allow it to cover extensive surfaces in real-time, without extra resources. The system implementation is based on the micro controllers “PIC18F4550” and “DSPIC 30F5011”, user interface was programmed under LINUX. The proposed system performance was compared with commercial Data Loggers, readings present a linear adjustment with R2=0.9656

IoT-Based Automatic Hydro-Organic Smart Farming System in Greenhouse with Solar Panels for Khok Nong Na

In this research, we propose IoT-Based Automatic Hydro-Organic Smart Farming System (AHOSFS) in Greenhouse with Solar Panels for Khok Nong Na, design and development with Internet of Things and solar energy. Data is collected from sensors for the following factors that affect plant growth: humidity, temperature, light, pH, EC, air quality, water temperature, and water level. The Firebase cloud-based application was used to collect all the data, and it enables farmers to monitor and control the system using their smartphones. The results showed AHOSFS can mix water and nutrient solution, which measure pH of 6.61 and EC of 1.23, control water level and mix nutrient solution in appropriate quantities. Green oak can thrive at pH levels between 6.0 and 7.0, EC levels between 1.1 and 1.7, and humidity level between 75 and 85. The sensor measured temperature value between 18 and 25 °C, which was adequate for the growth of green oak. Sensor also measured water temperature between 25 and 28 °C. The comparative value of cultivation in a hydro-organic system deployed between AB solution, organic fertilizer Formula 1 and Formula 2 found that organic fertilizer formula 1 has an effective and approximate AB solution at 81.64%. The fertilizer formula 2 has AB solution at 89.72%, which was more secure at a comparable level

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Research Trends on Greenhouse Engineering Using a Science Mapping Approach

Horticultural protected cultivation has spread throughout the world as it has proven to be extremely effective. In recent years, the greenhouse engineering research field has become one of the main research topics within greenhouse farming. The main objectives of the current study were to identify the major research topics and their trends during the last four decades by analyzing the co-occurrence network of keywords associated with greenhouse engineering publications. A total of 3804 pertinent documents published, in 1981-2021, were analyzed and discussed. China, the United States, Spain, Italy and the Netherlands have been the most active countries with more than 36% of the relevant literature. The keyword cluster analysis suggested the presence of five principal research topics: energy management and storage; monitoring and control of greenhouse climate parameters; automation of greenhouse operations through the internet of things (IoT) and wireless sensor network (WSN) applications; greenhouse covering materials and microclimate optimization in relation to plant growth; structural and functional design for improving greenhouse stability, ventilation and microclimate. Recent research trends are focused on real-time monitoring and automatic control systems based on the IoT and WSN technologies, multi-objective optimization approaches for greenhouse climate control, efficient artificial lighting and sustainable greenhouse crop cultivation using renewable energy

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings