A Survey on Automation Challenges and Opportunities for IoT based Agriculture

Agriculture automation is a major concern and a contentious issue in every country. This study provides a comprehensive assessment of the obstacles and potential associated with automating agricultural practises using IoT (Internet of Things) technology. It begins with an introduction that highlights the prior work and discusses the proposed proposal, which is centred on IoT and machine learning applications and breakthroughs in irrigation systems. The report digs into several IoT applications in agriculture, including crop and soil management, drone field surveillance, cattle and resource management, and pesticide/fertilizer tracking. It delves into the breakthroughs made possible by IoT and machine learning, particularly in smart irrigation systems, livestock monitoring, drone technology, precision agriculture, and integrated pest management. The paper thoroughly examines the challenges associated with automating irrigation practises, such as interoperability, data storage, connectivity, hardware and software maintenance, security concerns, data collection, environmental variability, cost, infrastructure, privacy, and adoption by small-scale farmers. The survey finishes by synthesising the important findings and emphasising the crucial need of overcoming these problems in order to successfully adopt IoT-driven agriculture automation

Precision Agriculture for Crop and Livestock Farming—Brief Review

In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.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

Smart high-yield tomato cultivation: precision irrigation system using the Internet of Things

The Internet of Things (IOT)-based smart farming promises ultrafast speeds and near real-time response. Precision farming enabled by the Internet of Things has the potential to boost efficiency and output while reducing water use. Therefore, IoT devices can aid farmers in keeping track crop health and development while also automating a variety of tasks (such as moisture level prediction, irrigation system, crop development, and nutrient levels). The IoT-based autonomous irrigation technique makes exact use of farmers’ time, money, and power. High crop yields can be achieved through consistent monitoring and sensing of crops utilizing a variety of IoT sensors to inform farmers of optimal harvest times. In this paper, a smart framework for growing tomatoes is developed, with influence from IoT devices or modules. With the help of IoT modules, we can forecast soil moisture levels and fine-tune the watering schedule. To further aid farmers, a smartphone app is currently in development that will provide them with crucial data on the health of their tomato crops. Large-scale experiments validate the proposed model’s ability to intelligently monitor the irrigation system, which contributes to higher tomato yields

LITERATURE REVIEW IOT SOFTWARE ARCHITECTURE ON AGRICULTURE

Context – Internet of Things (IoT) interrelates computing devices, machines, animals, or people and things that use the power of internet usage to utilize data to be much more usable. Food is one of the mandatory human needs to survive, and most of it is produced by agriculture. Using IoT in agriculture needs appropriate software architecture that plays a prominent role in optimizing the gain. Objective and Method – Implementing a solution in a specific field requires a particular condition that belongs to it. The objectives of this research study are to classify the state of the art IoT solution in the software architecture domain perspective. We have used the Evidence- Based Software Engineering (EBSE) and have 24 selected existing studies related to software architecture and IoT solutions to map to the software architecture needed on IoT solutions in agriculture. Result and Implications – The results of this study are the classification of various IoT software architecture solutions in agriculture. The highlighted field, especially in the areas of cloud, big data, integration, and artificial intelligence/machine learning. We mapped the agriculture taxonomy classification with IoT software architecture. For future work, we recommend enhancing the classification and mapping field to the utilization of drones in agriculture since drones can reach a vast area that is very fit for fertilizing, spraying, or even capturing crop images with live cameras to identify leaf disease

A review on IoT based precision irrigation planning and scheduling

Global warming and climate change are warnings showcasing water crisis. At the same time ever growing population is ultimatum to the food security. In span of such times, world has to be made a sustainable habitat. It is only possible when each ounce of resources is being measured and used judiciously. Maximum responsibility is on farmers and researchers of the world. In times of advanced technologies, Internet of Things (IoT) has surfaced as a saviour. IoT based systems have been stated as success in monitoring and control mechanisms. Thus, this paper was intended to review the control strategies and monitoring systems based on IoT. The literature incorporates basic information as well as recent trends in the field of irrigation management based on IoT

Assessment of Smart Mechatronics Applications in Agriculture: A Review

Smart mechatronics systems in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Impressive advances have been made since then in developing systems for use in modern agriculture. The aim of this study was to review smart mechatronics applications introduced in agriculture to date, and the different areas of the sector in which they are being employed. Various literature search approaches were used to obtain an overview of the current state-of-the-art, benefits, and drawbacks of smart mechatronics systems. Smart mechatronics modules and various networks applied in the processing of agricultural products were examined. Finally, relationships in the data retrieved were tested using a one-way analysis of variance on keywords and sources. The review revealed limited use of sophisticated mechatronics in the agricultural industry in practice at a time of falling production rates and a dramatic decline in the reliability of the global food supply. Smart mechatronics systems could be used in different agricultural enterprises to overcome these issues

IOF2020: Fostering business and software ecosystems for large-scale uptake of IoT in food and farming

The Internet of Things (IoT) is expected to be a real game changer that will drastically improve productivity and sustainability in food and farming. However, current IoT applications in this domain are still fragmentary and mainly used by a small group of early adopters. The Internet of Food and Farm 2020 Large-Scale Pilot (IoF2020) addresses the organizational and technological challenges to overcome this situation by fostering a large-scale uptake of IoT in the European food and farming domain. The heart of the project is formed by a balanced set of multi-actor trials that reflect the diversity of the food and farming domain. Each trial is composed of well-delineated use cases developing IoT solutions for the most relevant challenges of the concerned subsector. The project conducts 5 trials with a total of 19 use cases in arable, dairy, fruits, vegetables and meat production. IoF2020 embraces a lean multi-actor approach that combines the development of Minimal Viable Products (MVPs) in short iterations with the active involvement of various stakeholders. The architectural approach supports interoperability of multiple use case systems and reuse of IoT components across them. Use cases are also supported in developing business and solving governance issues. The IoF2020 ecosystem and collaboration space is established to boost the uptake of IoT in Food and Farming and pave the way for new innovations

Development of Automatic Mixing Process for Fertigation System in Rock Melon Cultivation

This work proposed an automatic mixing system of nutrient solution for rock melon fertigation according to the required electrical conductivity (EC) level. Compared to the manual practice, this automatic system will ensure continuous supply of mixed nutrient solution without the need to daily check and mix new nutrient. Thus, this easy to use and low cost automatic system will reduce the burden of the farmers. This system uses an EC sensor to automatically check the concentration level of the mixed nutrient solution. Other than that, the system only consists of electronic pumps for mixing process and an Arduino board as the controller. The controller will monitor the EC level and run the mixing process when the EC level is below the required level. By calibrating the EC sensors, the test shows that the automatic mixing system is able to accurately keep the mixed nutrient solution concentration in a 400 L mixing reservoir at several required levels