Past and Current Techniques and Solutions in IoT-based Farming

According to Rupanagudi, Ranjani, Nagaraj, Bhat and Thippeswamy (2015), smart farming represents the significant application of the modernized information and communications technology or ICT to agriculture. This is also known as third generation revolution. The plant breeding as well as the genetics revolution is being followed and hence the third green revolution is taking over the entire agricultural world on the basis of amalgamated application of the solutions of information and communications technology like sensors, actuators, the IoT or Internet of Things or IoT, big data, drones, robotics, unmanned aerial vehicle or precision equipment. Cloud computing is considered as one of the best, the safest and the most significant technology that helps in transferring data one from location to another easily and promptly over the Internet connection (Rupanagudi et al., 2015). Since, this technology is efficient, it is being utilized by several users, without any type of issue. Moreover, when this technology is combined with the Internet of Things, it provides several advantages or benefits to the users.

As per Ryu et al. (2015), the smart farming comprises of a real potential for delivering the more productive as well as sustainable production of agriculture. This is eventually done on the basis of a more correct, precise or resource efficient approach. More than 80% of the farmers are utilizing a specific type of smart farming technology in the world. This particular technology must give that specific farmer with extra value within the form of decision making or an efficient exploitation operation or management (Ryu et al., 2015). The smart farming technology is eventually related to the three interlinked fields of technology that are being addressed by the smart network. These three interconnected fields of technology are management information systems or MIS, precision agriculture, the agricultural automation as well as robotics.

Jayaraman, Yavari, Georgakopoulos, Morshed and Zaslavsky (2016), stated that Internet of things or IoT is responsible for providing a platform to smart farming. This particular technology provides the platform for the advancements within the information and communications technology on the basis of RFID or radio frequency identification system, ZigBee or short range wireless communications, Bluetooth, wireless fidelity and many more. The improvement of the farm productivity is necessary for the increment of the farm profit and even fulfilling the faster increasing demand of food, which is being incremented by the fast growth of population in the world (Jayaraman et al., 2016). The emerging technology of IoT like smart phones, wireless sensor network, network connected weather station or many more could be utilized for collating the huge amount of crop or environmental performance data.  The SmartFarmNet is the platform is used for smart farming.

The Most Effective Technology for Smart Farming

According to Balamurugan et al. (2016), the role of IoT in the agricultural sector is extremely vital and in today’s world, the advanced services like global positioning system as well as sensors, which enable the users for the purpose of communication with one another to analyze the data and even exchange the data amongst one another. Information technology is responsible for providing the services within the form of cloud to the agriculture. This combination provides the specialized skill service for the farmers about crop cultivation, fertilizer usage and many more. The agri-cloud based application enhances the agricultural production or availability of data.

As per Mahale and Sonavane (2016), with the help of IoT as well as the wireless sensor networks, the specific smart poultry farm is being enhanced drastically. The production of chicken is increasing every day with the standardized faming management or better manufacturing practice. With the combination of IoT, the smart farm becomes asset saving, cost effective, productive management and quality oriented technology for chicken farming. This provides a better solution of agriculture and smart farm to increase the overall benefits (Mahale & Sonavane, 2016). Currently, an Intelligent System is being implemented on the basis of smart phone and embedded framework to monitor the chicken farm for controlling the environmental parameters with technologies and smart devices.

Agrawal, Prieto, Ramos and Corchado (2016), state that the smart farming practices are of the major importance for all types of economies to foster the growth or development as well as tackling the problems such as food insecurity and hunger. This helps in ensuring the well being of the citizens in a country. The costly weighing machines for silos are extremely important in the current technology. There is a range of possibility of the technology of Internet of Things for reducing the costs after connecting with the farming data to the intelligent cloud services like the system of smart feeding (Agrawal et al., 2016). The quality and quantity of the grains within silos by the estimation of the volume of grains’ at the separate time instant; with humidity and temperature within the silos are being checked with this smart feeding system.

Xin and Zazueta (2016), stated that ICT driven smart farming is the latest trend in smart farming sector. An unprecedented opportunity and innovation is provided in this type of smart farming to improve the farming outcomes. The ongoing innovations like agricultural drones, social media, the Internet of Things or IoT, technology of cloud computing, big data and many more have solved the several challenges and provided the new opportunities for redefining and rethinking the role of the information and communication technology to achieve the outcomes of smart farming (Xin & Zazueta, 2016). The customized or cloud based solution for the farmers comprises of the components like data integration of the on farm sensor or data from the public source, knowledge-based software solution, service integration and many more.

Conclusion

According to Kamilaris, Gao, Prenafeta-Boldú & Ali (2016), the IoT frameworks are utilized for collecting, processing as well as analyzing the data stream within real time and then facilitating the provisions of smart solutions for providing the decision support to the users. The existing IoT based solutions are majorly domain dependent and provides stream processing as well as analytics to focus on the smart farms. The external parameters like weather regulations or conditions are considered in the smart farms (Kamilaris et al., 2016). The Agri-IoT technology is the large scale data analytics to ensure the seamless interoperability amongst the sensors, operations, farmers and processes.

On the basis of the literature review, the most effective and efficient four technologies that are used recently are precision agriculture, Agri-IoT semantic framework, cloud agriculture IoT and ICT based IoT for the betterment of smart farming (Lee, Hwang & Yoe, 2013).

The precision agriculture is the basic farming concept that helps in observing, measuring as well as responding to both inter fields and intra field variability within crops. The major objective of these precision agricultures is defining the DSS or decision support system to properly manage the farm and optimize the returns on inputs after preserving the resources.

The Agri-IoT semantic framework provides smart farming applications for the several heterogeneous sensor data stream within real-time (Sureephong, Wiangnak & Wicha, 2017). This semantic framework of Agri IoT could integrate the several cross-domain data stream and provide a whole semantic-processing pipeline after offering the framework for all the applications.

The cloud based IoT is also responsible for providing several important and significant applications that could reduce the complexities of smart farming (Prathibha, Hongal & Jyothi, 2017). Since, cloud is the most effective and efficient technology, the pests could be easily detected with this implementation of cloud computing and this combination with the internet of things would provide better crops for the users.

The data integration is easily done with the help of an ICT driven Internet of Things for the smart farming. It is a data driven, knowledge based and farmer centred hybrid cloud architecture for the smart farming that could provide better opportunities for the farmers.

Amongst the above mentioned four technologies, the most effective technology is the ICT driven IoT architecture for smart farming (Hussain, 2017). The on farm sensors are considered as the most effective of all, since; it helps in the reduction of architecture based complexities. As it is also a cloud based solution, the business services are easily integrated.

Conclusion

Therefore, from the above discussion, it can be concluded that with the current advancement in the technology of Internet of Things, it has become possible for processing the larger number of sensor data stream with the help of the large scale platforms of IoT. The frameworks of IoT help to collect analyze and process the data. The smart farming is the farming management concept on the basis of observation, measurement and the response of inter and intra field variability within the crops. Precision agriculture is the smart farming approach to agriculture after implementing sensors in the field and soil that transmit the real time data in the most cost effective manner. Various tools and data are used to analyze the farming practices in smart farming. Two important technologies are important for using IoT technology in agriculture and these are sensors and drones. The above research report has properly outlined the detailed discussion on Internet of Things in farming. Several significant technologies are being described in this research report. Agri-IoT technology is the most effective semantic framework that is described here for the smart farming application with IoT ability. Apart from this technology, some other technologies are also discussed in this research report for providing advantages to the smart farming.

References

Agrawal, H., Prieto, J., Ramos, C., & Corchado, J. M. (2016, September). Smart feeding in farming through iot in silos. In The International Symposium on Intelligent Systems Technologies and Applications (pp. 355-366). Springer, Cham.

Balamurugan, S., Divyabharathi, N., Jayashruthi, K., Bowiya, M., Shermy, R. P., & Shanker, R. (2016). Internet of agriculture: Applying IoT to improve food and farming technology. International Research Journal of Engineering and Technology (IRJET), 3(10), 713-719.

Hussain, F. (2017). Internet of things: Building blocks and business models (No. 978-3, pp. 319-55404). Springer International Publishing.

Jayaraman, P. P., Yavari, A., Georgakopoulos, D., Morshed, A., & Zaslavsky, A. (2016). Internet of things platform for smart farming: Experiences and lessons learnt. Sensors, 16(11), 1884.

Kamilaris, A., Gao, F., Prenafeta-Boldú, F. X., & Ali, M. I. (2016, December). Agri-IoT: A semantic framework for Internet of Things-enabled smart farming applications. In Internet of Things (WF-IoT), 2016 IEEE 3rd World Forum on (pp. 442-447). IEEE.

Lee, M., Hwang, J., & Yoe, H. (2013, December). Agricultural production system based on IoT. In Computational Science and Engineering (CSE), 2013 IEEE 16th International Conference on (pp. 833-837). IEEE.

Mahale, R. B., & Sonavane, S. S. (2016). Smart Poultry Farm Monitoring Using IOT and Wireless Sensor Networks. International Journal of Advanced Research in Computer Science, 7(3).

Prathibha, S. R., Hongal, A., & Jyothi, M. P. (2017, March). IOT Based Monitoring System in Smart Agriculture. In Recent Advances in Electronics and Communication Technology (ICRAECT), 2017 International Conference on (pp. 81-84). IEEE.

Rupanagudi, S. R., Ranjani, B. S., Nagaraj, P., Bhat, V. G., & Thippeswamy, G. (2015, January). A novel cloud computing based smart farming system for early detection of borer insects in tomatoes. In Communication, Information & Computing Technology (ICCICT), 2015 International Conference on (pp. 1-6). IEEE.

Ryu, M., Yun, J., Miao, T., Ahn, I. Y., Choi, S. C., & Kim, J. (2015, November). Design and implementation of a connected farm for smart farming system. In SENSORS, 2015 IEEE (pp. 1-4). IEEE.

Sureephong, P., Wiangnak, P., & Wicha, S. (2017, March). The comparison of soil sensors for integrated creation of IOT-based Wetting front detector (WFD) with an efficient irrigation system to support precision farming. In Digital Arts, Media and Technology (ICDAMT), International Conference on (pp. 132-135). IEEE.

Xin, J., & Zazueta, F. (2016). Technology trends in ICT–towards data-driven, farmer-centered and knowledge-based hybrid cloud architectures for smart farming. Agricultural Engineering International: CIGR Journal, 18(4), 275-279.