Journal articles: 'Internet of things, Precision agriculture'

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Alhasnawi, Bilal, Basil Jasim, and Bayadir Issa. "Internet of Things (IoT) for Smart Precision Agriculture." Iraqi Journal for Electrical and Electronic Engineering 16, no. 1 (April 12, 2020): 1–11. http://dx.doi.org/10.37917/ijeee.16.1.4.

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The scarcity of clean water resources around the globe has generated a need for their optimum utilization. Internet of Things (IoT) solutions, based on the application-specific sensors’ data acquisition and intelligent processing, are bridging the gaps between the cyber and physical worlds. IoT based smart irrigation management systems can help in achieving optimum water-resource utilization in the precision farming landscape. This paper presents an open-source technology-based smart system to predict the irrigation requirements of a field using the sensing of ground parameters like soil moisture, soil temperature, and environmental conditions along with the weather forecast data from the Internet. The sensing nodes, involved in the ground and environmental sensing, consider soil moisture, air temperature, and relative humidity of the crop field. This mainly focused on wastage of water, which is a major concern of the modern era. It is also time-saving, allows a user to monitor environmental data for agriculture using a web browser and Email, cost-effectiveness, environmental protection, low maintenance and operating cost and efficient irrigation service. The proposed system is made up of two parts: hardware and software. The hardware consists of a Base Station Unit (BSU) and several Terminal Nodes (TNs). The software is made up of the programming of the Wi-Fi network and the system protocol. In this paper, an MQTT (Message Queue Telemetry Transportation) broker was built on the BSU and TU board.

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Wang, Bin Peng. "The Design of Modern Agriculture Control System Based on Internet of Things." Applied Mechanics and Materials 513-517 (February 2014): 1519–22. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1519.

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This paper involves the modern agricultural application control system which is based on internet of things, and this intelligent management system uses intelligent control technology such as S7-300, GSM,WSN and Zigbee to realize the modernization of rural security, agricultural production and residents living fully intelligent managed. This system applies precision agriculture, digital image processing, wireless data transmission and other fields, really combining digital management technology with embedded technology. At the same time, this system which is based on internet of things is the necessary path of modern agriculture informatization strategy. With the mature development of technology of internet of things in modern society, modern agriculture application management system based on internet of things will bring new change to agriculture and high efficiency of agricultural production.

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Gill, Rana. "A Review on Various Techniques to Transform Traditional Farming to Precision Agriculture." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 131–35. http://dx.doi.org/10.17762/turcomat.v12i2.690.

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The agricultural sector is of great importance to fulfill food resources need of the country. Precision Agriculture (PA) with Internet of Things and Wireless Sensor Network is a transformation from traditional farming to smart farming. Wireless sensor networks and Internet of Things are considered as drivers to develop system which can change agriculture sector from manual to automatic. Advancement in the technology have pushed the growth of precision agriculture to very large extent despite of several challenges faced in this area. System for precision agriculture relies on hardware components mainly wireless sensors which act as a source for gathering of real time data. Depending upon the real time date retrieved by sensors automation in agriculture is done by adopting decision-based system. With Precision agriculture productivity is optimized by maintaining sustaniability as crop receives what is acutual requirement on the basis of new techniques and software platforms. This review article includes Inernet of Things (IoT), Wireless Sensors, Wireless communication and challenges faced in this area.

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Evett, Steven R., Susan A. O’Shaughnessy, Manuel A. Andrade, William P. Kustas, M. C. Anderson, H. H. Schomberg, and A. Thompson. "Precision Agriculture and Irrigation: Current U.S. Perspectives." Transactions of the ASABE 63, no. 1 (2020): 57–67. http://dx.doi.org/10.13031/trans.13355.

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Highlights.Precision agriculture (PA) applications in irrigation are stymied by lack of decision support systems.Modern PA relies on sensor systems and near real-time feedback for irrigation decision support and control.Sophisticated understanding of biophysics and biological systems now guides site-specific irrigation.The internet of things (IOT) enables new ways to increase yield per unit of water used and nutrient use efficiency. Keywords: Crop water productivity, Decision support system, Internet of things, Remote sensing, SCADA, Soil water content.

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Solanki, Rutvik. "IoT-based Precision Agriculture Platform: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1419–21. http://dx.doi.org/10.22214/ijraset.2021.38197.

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Abstract: Technological advancements such as the Internet of Things (IoT) and Artificial Intelligence (AI) are helping to boost the global agricultural sector as it is expected to grow by around seventy percent in the next two decades. There are sensor-based systems in place to keep track of the plants and the surrounding environment. This technology allows farmers to watch and control farm operations from afar, but it has a few limitations. For farmers, these technologies are prohibitively expensive and demand a high level of technological competence. Besides, Climate change has a significant impact on crops because increased temperatures and changes in precipitation patterns increase the likelihood of disease outbreaks, resulting in crop losses and potentially irreversible plant destruction. Because of recent advancements in IoT and Cloud Computing, new applications built on highly innovative and scalable service platforms are now being developed. The use of Internet of Things (IoT) solutions has enormous promise for improving the quality and safety of agricultural products. Precision farming's telemonitoring system relies heavily on Internet of Things (IoT) platforms; therefore, this article quickly reviews the most common IoT platforms used in precision agriculture, highlighting both their key benefits and drawbacks

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Ali, Terteil A. A. "Precision Agriculture Monitoring System using Internet of Things (IoT)." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 2961–70. http://dx.doi.org/10.22214/ijraset.2018.4493.

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S., Anulekshmi. "Comprehensive Study and Research on Wireless Sensor Network and Internet of Things for Precision Agriculture." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (March 31, 2020): 150–58. http://dx.doi.org/10.5373/jardcs/v12i4/20201427.

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Spachos, Petros. "Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices." IoT 1, no. 1 (February 21, 2020): 5–20. http://dx.doi.org/10.3390/iot1010002.

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Precision Agriculture (PA) is an ever-expanding field that takes modern technological advancements and applies it to farming practices to reduce waste and increase output. One advancement that can play a significant role in achieving precision agriculture is wireless technology, and specifically the Internet of Things (IoT) devices. Small, inch scale and low-cost devices can be used to monitor great agricultural areas. In this paper, a system for precision viticulture which uses IoT devices for real-time monitoring is proposed. The different components of the system are programmed properly and the interconnection between them is designed to minimize energy consumption. Wireless sensor nodes measure soil moisture and soil temperature in the field and transmit the information to a base station. If the conditions are optimal for a disease or pest to occur, a drone flies towards the area. When the drone is over the node, pictures are captured and then it returns to the base station for further processing. The feasibility of the system is examined through experimentation in a realistic scenario.

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Vuran, Mehmet C., Abdul Salam, Rigoberto Wong, and Suat Irmak. "Internet of underground things in precision agriculture: Architecture and technology aspects." Ad Hoc Networks 81 (December 2018): 160–73. http://dx.doi.org/10.1016/j.adhoc.2018.07.017.

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Lin, Na, Xuping Wang, Yihao Zhang, Xiangpei Hu, and Junhu Ruan. "Fertigation management for sustainable precision agriculture based on Internet of Things." Journal of Cleaner Production 277 (December 2020): 124119. http://dx.doi.org/10.1016/j.jclepro.2020.124119.

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Jin, Xue-Bo, Xing-Hong Yu, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, and Jian-Lei Kong. "Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System." Sustainability 12, no. 4 (February 14, 2020): 1433. http://dx.doi.org/10.3390/su12041433.

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Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.

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Kitouni, Ilham, Djamel Benmerzoug, and Fouzi Lezzar. "Smart Agricultural Enterprise System Based on Integration of Internet of Things and Agent Technology." Journal of Organizational and End User Computing 30, no. 4 (October 2018): 64–82. http://dx.doi.org/10.4018/joeuc.2018100105.

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This article describes how the Internet of Things (IoT) and its smart objects will be the fundamental building blocks for the creation of smart pervasive systems. Precision Agriculture can be defined as the use of information, communications technologies and electronic devices in agricultural practice, to improve agricultural production. This article addresses the problem of modelling Precision Agriculture systems. The authors propose an Agent-based approach for an effective integration of the IoT technology in Agricultural Enterprise systems. The proposed approach is based on Agent Interaction Protocols (AiP) through which they specify complex services of the system by recognizing larger chunks that have a meaning in the application domain. The AiP supports the modelling of composite services as entities whose business logic involves a number of interactions among more elementary service components. The authors present an agent-based system architecture whose main goal is to address interoperability issues between heterogeneous IoT-based services by offering a harmonized API.

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Korotchenya, V. M., G. I. Lichman, and I. G. Smirnov. "Digitalization of Technological Processes of Crop Production in Russia." Agricultural Machinery and Technologies 13, no. 1 (February 21, 2019): 14–20. http://dx.doi.org/10.22314/2073-7599-2018-13-1-14-20.

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Currently, the influence of program documents on digital agriculture development is rather great in our country. Within the framework of the European Association of Agricultural Mechanical Engineering, a relevant definition of agriculture 4.0 has been elaborated and introduced.Research purpose: offering general recommendations on the digitalization of agriculture in RussiaMaterials and methods. The authors make use of the normative approach: the core of digital agriculture is compared with the current state of the agricultural sector in Russia.Results and discussion. The analysis has found that digital agriculture (agriculture 4.0 and 5.0) is based on developed mechanized technologies (agriculture 2.0), precision agriculture technologies (agriculture 3.0), the use of such digital technologies and technical means as the Internet of things, artificial intelligence, and robotics. The success of introducing digital agriculture depends on the success of all the three levels of the system. However, the problem of the lack of agricultural machinery indicates insufficient development of mechanized technologies; poor implementation of precision agriculture technologies means the lack of experience of using these technologies by the majority of farms in our country; an insufficient number of leading Russian IT companies (such as Amazon, Apple, Google, IBM, Intel, Microsoft etc.) weakens the country’s capacity in making a breakthrough in the development of the Internet of things, artificial intelligence, and robotics.Conclusions.The authors have identified the need to form scientific approaches to the digitization of technological operations used in the cultivation of agricultural crops and classified precision agriculture technologies. They have underlined that the digitization of agricultural production in Russia must be carried out along with intensified mechanization (energy saturation); also, to introduce technologies of precision agriculture and digital agriculture, it is necessary to organize state-funded centers for training farmers in the use of these technologies. Finally, it is necessary to take measures to strengthen the development of the IT sphere, as well as formulate an integral approach to the problem of digitalization.

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Gsangaya, K. R., S. S. H. Hajjaj, M. T. H. Sultan, and L. S. Hua. "Portable, wireless, and effective internet of things-based sensors for precision agriculture." International Journal of Environmental Science and Technology 17, no. 9 (April 16, 2020): 3901–16. http://dx.doi.org/10.1007/s13762-020-02737-6.

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Feng, Xiang, Fang Yan, and Xiaoyu Liu. "Study of Wireless Communication Technologies on Internet of Things for Precision Agriculture." Wireless Personal Communications 108, no. 3 (May 8, 2019): 1785–802. http://dx.doi.org/10.1007/s11277-019-06496-7.

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Nugroho, B. D. A., and H. K. Aliwarga. "RiTx; Integrating among Field Monitoring System (FMS), Internet of Things (IOT) and agriculture for precision agriculture." IOP Conference Series: Earth and Environmental Science 335 (October 28, 2019): 012022. http://dx.doi.org/10.1088/1755-1315/335/1/012022.

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Ferrández-Pastor, Francisco, Juan García-Chamizo, Mario Nieto-Hidalgo, Jerónimo Mora-Pascual, and José Mora-Martínez. "Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture." Sensors 16, no. 7 (July 22, 2016): 1141. http://dx.doi.org/10.3390/s16071141.

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Ahmed, Nurzaman, Debashis De, and Iftekhar Hussain. "Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas." IEEE Internet of Things Journal 5, no. 6 (December 2018): 4890–99. http://dx.doi.org/10.1109/jiot.2018.2879579.

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Li, Juanjuan. "Design and Realization of Greenhouse Sensor Intelligent Management System Based on Internet of Things." International Journal of Online Engineering (iJOE) 13, no. 05 (May 14, 2017): 80. http://dx.doi.org/10.3991/ijoe.v13i05.7051.

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Traditional and inefficient agricultural production methods cannot meet the modern agriculture requirements of safe, high quality, efficiency and productivity. The technology of Internet of Things is introduced into the field of agriculture, and the agricultural industrialization and information technology has an unprecedented opportunity. The relevant literature is read, the actual scene is investigated and the needs of agricultural field monitoring are identified. In the meanwhile, the development trend of Internet of Things and facility agricultural monitoring system is analyzed and system performance indicators that meet the actual requirements are developed. Moreover, the overall program of the system is designed and the three-tier architecture of Internet of Things system based on sensor technology, wireless communication technology, and configuration monitoring technology is constructed. The structure of the three layers of the sensing layer, transmission layer and application layer is analyzed, and the greenhouse sensor intelligent management system based on Internet of Things is designed. The performance of the system is tested in the laboratory. The test items include remote monitoring effect, information acquisition precision and system overall coordination. The results showed that the system is reasonable, the structure is compact, the network layer is reliable, and the performance is stable. Meanwhile, the application layer is rich in functionality, the interface is beautiful, the data processing is intelligent and the operability is strong. As last, it is concluded that the system meets system design requirements and expected performance specifications.

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Patil, Suhas M., and Sakkaravarthi R. "INTERNET OF THINGS BASED SMART AGRICULTURE SYSTEM USING PREDICTIVE ANALYTICS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 148. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19601.

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Due to the use of internet of things (IoT) devices, communication between different things is effective. The application of IoT in agriculture industryplays a key role to make functionalities easy. Using the concept of IoT and wireless sensor network (WSN), smart farming system has been developedin many areas of the world. Precision farming is one of the branches comes forward in this aspect. Many researchers have developed monitoring andautomation system for different functionalities of farming. Using WSN, data acquisition and transmission between IoT devices deployed in farms will be easy. In proposed technique, Kalman filter (KF) is used with prediction analysis to acquire quality data without any noise and to transmit this data for cluster-based WSNs. Due to the use of this approach, the quality of data used for analysis is improved as well as data transfer overhead is minimized in WSN application. Decision tree is used for decision making using prediction analytics for crop yield prediction, crop classification, soil classification, weather prediction, and crop disease prediction. IoT components, such as and cube (IOT Gateway) and Mobius (IOT Service platform), are integrated in proposed system to provide smart solution for crop growth monitoring to users.

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Lao, Fei, and Guo Xin Li. "The Design and Implementation of Crop Growing Environment Monitoring System Based on the Internet of Things." Advanced Materials Research 912-914 (April 2014): 1440–43. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1440.

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Because the extensive management of the tradational agriculture hinders the development of the agriculture,we advise the system based on the inteent of things,which design and implent the crop growing enviornment.This article describes the meaning and the functions of the system in details,which also describes the architecture ,hardware components and software design.The design of the system promotes the rapid development of the precision agriculture.

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Fahad, Muhammad, Tariq Javid, Hira Beenish, Adnan Ahmed Siddiqui, and Ghufran Ahmed. "Extending ONTAgri with Service-Oriented Architecture towards Precision Farming Application." Sustainability 13, no. 17 (August 31, 2021): 9801. http://dx.doi.org/10.3390/su13179801.

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The computer science perspective of ontology refers to ontology as a technology, however, with a different perspective in terms of interrogations and concentrations to construct engineering models of reality. Agriculture-centered architectures are among rich sources of knowledge that are developed, preserved, and released for farmers and agro professionals. Many researchers have developed different variants of existing ontology-based information systems. These systems are primarily picked agriculture-related ontological strategies based on activities such as crops, weeds, implantation, irrigation, and planting, to name a few. By considering the limitations on agricultural resources in the ONTAgri scenario, in this paper, an extension of ontology is proposed. The extended ONTAgri is a service-oriented architecture that connects precision farming with both local and global decision-making methods. These decision-making methods are connected with the Internet of Things systems in parallel for the input processing of system ontology. The proposed architecture fulfills the requirements of Agriculture 4.0. The significance of the proposed approach aiming to solve a multitude of agricultural problems being faced by the farmers is successfully demonstrated through SPARQL queries.

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Ram, C. Ramasamy Sankar, S. Ravimaran, R. Santhana Krishnan, E. Golden Julie, Y. Harold Robinson, Raghvendra Kumar, Le Hoang Son, Pham Huy Thong, Nguyen Quang Thanh, and Mahmoud Ismail. "Internet of Green Things with autonomous wireless wheel robots against green houses and farms." International Journal of Distributed Sensor Networks 16, no. 6 (June 2020): 155014772092347. http://dx.doi.org/10.1177/1550147720923477.

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Nowadays, smart farming involves the integration of advanced technologies that incorporate low-cost robots to meet the required knowledge and maintain the health of plants in farming. Technologies like precision agriculture are also used to optimize resources based on the field condition. Internet of Green Things is also one of the technologies to integrate and share the information between people and healthy farm things. Internet of Green Things gives the information like soil moisture, temperature, humidity, and nutrient level by means of respective sensors. Monitoring and information gathering in green houses with the help of robots is a tedious and expensive process. In this connection, information is shared among low-cost robots encouraging data availability of the current state of a plant with other robots. This will emphasize the monitoring of green houses in a well-organized way. In this article, a Flask-based framework through Raspberry Pi is proposed for interoperability among the low-cost ESP8266 robots. Data gathering is performed by smart robots that are accessible through Message Queuing Telemetry Transport subscribes by means of Representational State Transfer Application Programming Interface. A cloud-like database server is provided to stock up the data. The integration of robotics with Internet of Green Things gains more advantage in gathering about spatial information data that are connected with the irrigation. Visualization techniques and perspectives based on Internet of Green Things for precision agriculture in the field of farming are highlighted.

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Ferrández-Pastor, Francisco, Juan García-Chamizo, Mario Nieto-Hidalgo, and José Mora-Martínez. "Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context." Sensors 18, no. 6 (May 28, 2018): 1731. http://dx.doi.org/10.3390/s18061731.

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Torky, Mohamed, and Aboul Ella Hassanein. "Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges." Computers and Electronics in Agriculture 178 (November 2020): 105476. http://dx.doi.org/10.1016/j.compag.2020.105476.

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Lin, Hanhui, Ken Cai, Huazhou Chen, and ZhaoFeng Zeng. "The Construction of a Precise Agricultural Information System Based on Internet of Things." International Journal of Online Engineering (iJOE) 11, no. 6 (November 5, 2015): 10. http://dx.doi.org/10.3991/ijoe.v11i6.4847.

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The promotion of the Internet of Things (IOT) in agriculture is an important symbol in the modern agricultural industry. It can efficiently lower the labor consumption and exert a positive impact on farmlands through wireless sensor networks. It can precisely acquire data on crops and the environment to achieve the scientific cultivation and management of the production equipment by means of automation, intelligence, and remote control and to advance the transformation of agricultural development in modern times. An intelligent system of high precision, which is based on the IOT, is formulated in this study. Such system applies Advanced RISC Machines (ARM) as its built-in gateway, with such carriers as Bluetooth, 2.4 GHz, Zigbee, Global System for Mobile Communications (GSM), Wi-Fi, and others, to establish wireless sensor networks and manage the agricultural production through remote control and intelligent management. The experiment suggests that the system can efficiently supervise and control the multiple environmental parameters and farmland equipment to meet the requirement of agricultural production.

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Syrový, Tomáš, Robert Vik, Silvan Pretl, Lucie Syrová, Jiří Čengery, Aleš Hamáček, Lubomír Kubáč, and Ladislav Menšík. "Fully Printed Disposable IoT Soil Moisture Sensors for Precision Agriculture." Chemosensors 8, no. 4 (December 6, 2020): 125. http://dx.doi.org/10.3390/chemosensors8040125.

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Digitization of industrial processes using new technologies (IoT—Internet of Things, IoE—Internet of Everything), including the agriculture industry, are globally gaining growing interest. The precise management of production inputs is essential for many agricultural companies because limited or expensive sources of water and nutrients could make sustainable production difficult. For these reasons, precise data from fields, plants, and greenhouses have become more important for decision making and for the proper dosage of water and nutrients. On the market are a variety of sensors for monitoring environmental parameters within a precise agricultural area. However, the high price, data storage/transfer functionality are limiting so cost-effective products capable to transfer data directly to farmers via wireless IoT networks are required. Within a given scope, low-price sensor elements with an appropriate level of sensor response are required. In the presented paper, we have developed fully printed sensor elements and a dedicated measuring/communicating unit for IoT monitoring of soil moisture. Various fabrication printing techniques and a variety of materials were used. From the performed study, it is obvious that fully printed sensor elements based on cheap and environmentally friendly carbon layers printed on the wood substrate can compete with conventionally made sensors based on copper.

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Wu, Guang Hua, Feng Liu, Jun Xing Li, and Wei Wang. "Environmental Monitoring System Designing: A Internet of Things Approach." Applied Mechanics and Materials 644-650 (September 2014): 3342–45. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3342.

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The key propose of this paper is to design a IoT(Internet of Thing) based farm environmental monitoring system and to realize the automate management of agriculture and the implementation of precision production. The system consists of three layers which are sensor layer, transmission layer and application layer, respectively. The modular structure is adopted to develop coordinator node and router node which have flexible structures and strong versatility, the node connects with sensors through standard analog interface, on the basis of which, high reliability, flexible Wireless Sensor Network (WSN) is built, the WSN can perceive environment information for greenhouse tomato growth in a real time way and transmit the data to the remote server management system reliably. Field experiments show that WSN is stable, reliable and provides basis for the scientific management of farm.

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Akhtar, Mohammad Nishat, Abdurrahman Javid Shaikh, Ambareen Khan, Habib Awais, Elmi Abu Bakar, and Abdul Rahim Othman. "Smart Sensing with Edge Computing in Precision Agriculture for Soil Assessment and Heavy Metal Monitoring: A Review." Agriculture 11, no. 6 (May 21, 2021): 475. http://dx.doi.org/10.3390/agriculture11060475.

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With the implementation of the Internet of Things, the agricultural domain has become data-driven, allowing for well-timed and cost-effective farm management while remaining environmentally sustainable. Thus, the incorporation of Internet of Things in the agricultural domain is the need of the hour for developing countries whose gross domestic product primarily depends on the farming sector. It is worth highlighting that developing nations lack the infrastructure for precision agriculture; therefore, it has become necessary to come up with a methodological paradigm which can accommodate a complete model to connect ground sensors to the compute nodes in a cost-effective way by keeping the data processing limitations and constraints in consideration. In this regard, this review puts forward an overview of the state-of-the-art technologies deployed in precision agriculture for soil assessment and pollutant monitoring with respect to heavy metal in agricultural soil using various sensors. Secondly, this manuscript illustrates the processing of data generated from the sensors. In this regard, an optimized method of data processing derived from cloud computing has been shown, which is called edge computing. In addition to this, a new model of high-performance-based edge computing is also shown for efficient offloading of data with smooth workflow optimization. In a nutshell, this manuscript aims to open a new corridor for the farming sector in developing nations by tackling challenges and providing substantial consideration.

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Rodríguez-Robles, Javier, Álvaro Martin, Sergio Martin, José A. Ruipérez-Valiente, and Manuel Castro. "Autonomous Sensor Network for Rural Agriculture Environments, Low Cost, and Energy Self-Charge." Sustainability 12, no. 15 (July 23, 2020): 5913. http://dx.doi.org/10.3390/su12155913.

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Over the last years, existing technologies have been applied to agricultural environments, resulting in new precision agriculture systems. Some of the multiple profits of developing new agricultural technologies and applications include the cost reduction around the building and deployment of them, together with more energy-efficient consumption. Therefore, agricultural precision systems focus on developing better, easier, cheaper, and overall more efficient ways of handling agricultural monitoring and actuation. To achieve this vision, we use a set of technologies such as Wireless Sensor Networks, Sensors devices, Internet of Things, or data analysis. More specifically, in this study, we proposed a combination of all these technologies to design and develop a prototype of a precision agriculture system for medium and small agriculture plantations that highlights two major advantages: efficient energy management with self-charging capabilities and a low-cost policy. For the development of the project, several prototype nodes were built and deployed within a sensor network connected to the cloud as a self-powered system. The final target of this system is, therefore, to gather environment data, analyze it, and actuate by activating the watering installation. An analysis of the exposed agriculture monitoring system, in addition to results, is exposed in the paper.

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Touseau, Lionel, and Nicolas Sommer. "Contribution of the Web of Things and of the Opportunistic Computing to the Smart Agriculture: A Practical Experiment." Future Internet 11, no. 2 (February 1, 2019): 33. http://dx.doi.org/10.3390/fi11020033.

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With the emergence of the Internet of Things, environmental sensing has been gaining interest, promising to improve agricultural practices by facilitating decision-making based on gathered environmental data (i.e., weather forecasting, crop monitoring, and soil moisture sensing). Environmental sensing, and by extension what is referred to as precision or smart agriculture, pose new challenges, especially regarding the collection of environmental data in the presence of connectivity disruptions, their gathering, and their exploitation by end-users or by systems that must perform actions according to the values of those collected data. In this paper, we present a middleware platform for the Internet of Things that implements disruption tolerant opportunistic networking and computing techniques, and that makes it possible to expose and manage physical objects through Web-based protocols, standards and technologies, thus providing interoperability between objects and creating a Web of Things (WoT). This WoT-based opportunistic computing approach is backed up by a practical experiment whose outcomes are presented in this article.

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Triantafyllou, Anna, Panagiotis Sarigiannidis, and Stamatia Bibi. "Precision Agriculture: A Remote Sensing Monitoring System Architecture †." Information 10, no. 11 (November 9, 2019): 348. http://dx.doi.org/10.3390/info10110348.

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Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece.

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Gusev, Aleksey, Egor Skvorcov, and Ekaterina Morozova. "Studying the international practice of introducing precision farming technologies based on national programs for the development of the agrarian sector of foreign countries." Russian Journal of Management 8, no. 3 (November 24, 2020): 121–25. http://dx.doi.org/10.29039/2409-6024-2020-8-3-121-125.

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The relevance of the research lies in the insufficiently studied theoretical aspects of the application of precision farming technologies. The development programs of the industry in countries with developed agriculture contain separate elements for the development of precision farming technologies. The aim is to study the international practice of introducing precision farming technologies on the basis of national programs for the development of the agrarian sector of countries with developed agriculture. The United States occupies a leading position in the development of precision farming technologies. This became possible thanks to the implementation of the Food and Agriculture Cyber Informatics and Tools (FACT) program, as well as the development of these technologies by private companies (Ag Leader Technology; AgJunction, Inc; CropMetrics LLC, etc.). In the People's Republic of China, in the Thirteenth Five-Year Plan for the Economic and Social Development of the Republic, Article 4 proclaims a course for the modernization of agriculture, which is designed for 2015-2020. It is planned to introduce a regional pilot project in the field of precision farming technologies based on IoT, increasing the level of intelligence and precision of agriculture. Japan has implemented the Strategic Innovation Promotion Program (SIP) for the next generation of agriculture, forestry and fisheries. Its main tasks include an automatic travel system for agricultural machinery under human supervision (by 2018), as well as an unmanned system for agricultural machinery with remote monitoring (by 2020). In total, 15.6 billion yen (11.5 billion rubles) was allocated for the implementation of these tasks in the period from 2014 to 2018. In Germany, 14 digital innovation parks have been created, aimed at developing both precision farming technologies and technologies of the Internet of things, big data and others.

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Zervopoulos, Alexandros, Athanasios Tsipis, Aikaterini Georgia Alvanou, Konstantinos Bezas, Asterios Papamichail, Spiridon Vergis, Andreana Stylidou, et al. "Wireless Sensor Network Synchronization for Precision Agriculture Applications." Agriculture 10, no. 3 (March 24, 2020): 89. http://dx.doi.org/10.3390/agriculture10030089.

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The advent of Internet of Things has propelled the agricultural domain through the integration of sensory devices, capable of monitoring and wirelessly propagating information to producers; thus, they employ Wireless Sensor Networks (WSNs). These WSNs allow real time monitoring, enabling intelligent decision-making to maximize yields and minimize cost. Designing and deploying a WSN is a challenging and multivariate task, dependent on the considered environment. For example, a need for network synchronization arises in such networks to correlate acquired measurements. This work focuses on the design and installation of a WSN that is capable of facilitating the sensing aspects of smart and precision agriculture applications. A system is designed and implemented to address specific design requirements that are brought about by the considered environment. A simple synchronization scheme is described to provide time-correlated measurements using the sink node’s clock as reference. The proposed system was installed on an olive grove to assess its effectiveness in providing a low-cost system, capable of acquiring synchronized measurements. The obtained results indicate the system’s overall effectiveness, revealing a small but expected difference in the acquired measurements’ time correlation, caused mostly by serial transmission delays, while yielding a plethora of relevant environmental conditions.

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G. S. Campos, Nidia, Atslands R. Rocha, Rubens Gondim, Ticiana L. Coelho da Silva, and Danielo G. Gomes. "Smart & Green: An Internet-of-Things Framework for Smart Irrigation." Sensors 20, no. 1 (December 29, 2019): 190. http://dx.doi.org/10.3390/s20010190.

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Irrigation is one of the most water-intensive agricultural activities in the world, which has been increasing over time. Choosing an optimal irrigation management plan depends on having available data in the monitoring field. A smart agriculture system gathers data from several sources; however, the data are not guaranteed to be free of discrepant values (i.e., outliers), which can damage the precision of irrigation management. Furthermore, data from different sources must fit into the same temporal window required for irrigation management and the data preprocessing must be dynamic and automatic to benefit users of the irrigation management plan. In this paper, we propose the Smart&Green framework to offer services for smart irrigation, such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management enriched by the prediction of soil moisture. Outlier removal techniques allow for more precise irrigation management. For fields without soil moisture sensors, the prediction model estimates the matric potential using weather, crop, and irrigation information. We apply the predicted matric potential approach to the Van Genutchen model to determine the moisture used in an irrigation management scheme. We can save, on average, between 56.4% and 90% of the irrigation water needed by applying the Zscore, MZscore and Chauvenet outlier removal techniques to the predicted data.

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Lamtzidis, Odysseas, Dennis Pettas, and John Gialelis. "A Novel Combination of Distributed Ledger Technologies on Internet of Things: Use Case on Precision Agriculture." Applied System Innovation 2, no. 3 (September 18, 2019): 30. http://dx.doi.org/10.3390/asi2030030.

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Internet-of-Things (IoT) is an enabling technology for numerous initiatives worldwide such as manufacturing, smart cities, precision agriculture, and eHealth. The massive field data aggregation of distributed administered IoT devices allows new insights and actionable information for dynamic intelligent decision-making. In such distributed environments, data integrity, referring to reliability and consistency, is deemed insufficient and requires immediate facilitation. In this article, we introduce a distributed ledger (DLT)-based system for ensuring IoT data integrity which securely processes the aggregated field data. Its uniqueness lies in the embedded use of IOTA’s ledger, called “The Tangle”, used to transmit and store the data. Our approach shifts from a cloud-centric IoT system, where the Super nodes simply aggregate and push data to the cloud, to a node-centric system, where each Super node owns the data pushed in a distributed and decentralized database (i.e., the Tangle). The backend serves as a consumer of data and a provider of additional resources, such as administration panel, analytics, data marketplace, etc. The proposed implementation is highly modularand constitutes a significant contribution to the Open Source communities, regarding blockchain and IoT.

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Mazon-Olivo, Bertha, Dixys Hernández-Rojas, José Maza-Salinas, and Alberto Pan. "Rules engine and complex event processor in the context of internet of things for precision agriculture." Computers and Electronics in Agriculture 154 (November 2018): 347–60. http://dx.doi.org/10.1016/j.compag.2018.09.013.

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Khanna, Abhishek, and Sanmeet Kaur. "Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture." Computers and Electronics in Agriculture 157 (February 2019): 218–31. http://dx.doi.org/10.1016/j.compag.2018.12.039.

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Monteleone, Sergio, Edmilson Alves de Moraes, Brenno Tondato de Faria, Plinio Thomaz Aquino Junior, Rodrigo Filev Maia, André Torre Neto, and Attilio Toscano. "Exploring the Adoption of Precision Agriculture for Irrigation in the Context of Agriculture 4.0: The Key Role of Internet of Things." Sensors 20, no. 24 (December 11, 2020): 7091. http://dx.doi.org/10.3390/s20247091.

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In recent years, the concept of Agriculture 4.0 has emerged as an evolution of precision agriculture (PA) through the diffusion of the Internet of things (IoT). There is a perception that the PA adoption is occurring at a slower pace than expected. Little research has been carried out about Agriculture 4.0, as well as to farmer behavior and operations management. This work explores what drives the adoption of PA in the Agriculture 4.0 context, focusing on farmer behavior and operations management. As a result of a multimethod approach, the factors explaining the PA adoption in the Agriculture 4.0 context and a model of irrigation operations management are proposed. Six simulation scenarios are performed to study the relationships among the factors involved in irrigation planning. Empirical findings contribute to a better understanding of what Agriculture 4.0 is and to expand the possibilities of IoT in the PA domain. This work also contributes to the discussion on Agriculture 4.0, thanks to multidisciplinary research bringing together the different perspectives of PA, IoT and operations management. Moreover, this research highlights the key role of IoT, considering the farmer’s possible choice to adopt several IoT sensing technologies for data collection.

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Neethirajan, Suresh. "2 Biosensors - New Frontiers in Animal Welfare." Journal of Animal Science 97, Supplement_2 (July 2019): 2. http://dx.doi.org/10.1093/jas/skz122.002.

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Abstract Glocalization and ecological pressures increase the risk of a biological incident of national significance in farmed animal health management, such as the emergence of a novel infectious agent and/or a global pandemic. An important strategy to monitor such an incident is to anticipate when and where it may occur to enable a timely and well-informed measurement as response. As agreed by researchers, industry experts and market watchers, the fourth revolution in agriculture has begun. Novel technologies such as Internet of Things, SMART Agriculture Initiative, Precision Agriculture, and development of apps for disease surveillance are emerging to drive efficiency. As such, rapid on-farm diagnostics of novel infectious agents and global pandemics using advanced diagnostic methods and tools, as Smart (Precision) Farming and Agricultural IoT (Internet of Things), is significant as part of the fourth agricultural revolution. This talk will discuss examples of innovations such as arrays of diagnostic platforms and wearables as nanosensors using biotechnology to detect 1) biomarker proteins; and 2) antibodies targeting specifically the proteins in multiple livestock sectors: dairy animals; poultry; and pork sectors to diagnose diseases and infections. With novel infectious agents and global pandemics on the rise in farmed livestock, it is imperative to develop detection tools that can predict when an incident is likely to occur, determine the exposed population, inform diagnosis and treatment decisions, and forecast the incident’s potential impact on human and animal populations. The amount of time farmers will have to spend on the farm to make decisions will be significantly reduced. With automated and rapid disease monitoring, farmers will be able to receive real-time information on their livestock, increasing both the efficiency and sustainability for farming and livestock production.

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Kamienski, Carlos, Juha-Pekka Soininen, Markus Taumberger, Ramide Dantas, Attilio Toscano, Tullio Salmon Cinotti, Rodrigo Filev Maia, and André Torre Neto. "Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture." Sensors 19, no. 2 (January 11, 2019): 276. http://dx.doi.org/10.3390/s19020276.

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The smart management of freshwater for precision irrigation in agriculture is essential for increasing crop yield and decreasing costs, while contributing to environmental sustainability. The intense use of technologies offers a means for providing the exact amount of water needed by plants. The Internet of Things (IoT) is the natural choice for smart water management applications, even though the integration of different technologies required for making it work seamlessly in practice is still not fully accomplished. The SWAMP project develops an IoT-based smart water management platform for precision irrigation in agriculture with a hands-on approach based on four pilots in Brazil and Europe. This paper presents the SWAMP architecture, platform, and system deployments that highlight the replicability of the platform, and, as scalability is a major concern for IoT applications, it includes a performance analysis of FIWARE components used in the Platform. Results show that it is able to provide adequate performance for the SWAMP pilots, but requires specially designed configurations and the re-engineering of some components to provide higher scalability using less computational resources.

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Shafi, Uferah, Rafia Mumtaz, José García-Nieto, Syed Ali Hassan, Syed Ali Raza Zaidi, and Naveed Iqbal. "Precision Agriculture Techniques and Practices: From Considerations to Applications." Sensors 19, no. 17 (September 2, 2019): 3796. http://dx.doi.org/10.3390/s19173796.

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Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.

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Taşkın, Deniz, Cem Taşkın, and Selçuk Yazar. "Developing a Bluetooth Low Energy Sensor Node for Greenhouse in Precision Agriculture as Internet of Things Application." Advances in Science and Technology Research Journal 12, no. 4 (December 1, 2018): 88–96. http://dx.doi.org/10.12913/22998624/100342.

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Sishodia, Rajendra P., Ram L. Ray, and Sudhir K. Singh. "Applications of Remote Sensing in Precision Agriculture: A Review." Remote Sensing 12, no. 19 (September 24, 2020): 3136. http://dx.doi.org/10.3390/rs12193136.

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Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

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Antony, Anish Paul, Kendra Leith, Craig Jolley, Jennifer Lu, and Daniel J. Sweeney. "A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture." Sustainability 12, no. 9 (May 6, 2020): 3750. http://dx.doi.org/10.3390/su12093750.

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In order to feed a growing global population projected to increase to 9 billion by 2050, food production will need to increase from its current level. The bulk of this growth will need to come from smallholder farmers who rely on generational knowledge in their farming practices and who live in locations where weather patterns and seasons are becoming less predictable due to climate change. The expansion of internet-connected devices is increasing opportunities to apply digital tools and services on smallholder farms, including monitoring soil and plants in horticulture, water quality in aquaculture, and ambient environments in greenhouses. In combination with other food security efforts, internet of things (IoT)-enabled precision smallholder farming has the potential to improve livelihoods and accelerate low- and middle-income countries’ journey to self-reliance. Using a combination of interviews, surveys and site visits to gather information, this research presents a review of the current state of the IoT for on-farm measurement, cases of successful IoT implementation in low- and middle-income countries, challenges associated with implementing the IoT on smallholder farms, and recommendations for practitioners.

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Saleem, Rana Muhammad, Rafaqat Kazmi, Imran Sarwar Bajwa, Amna Ashraf, Shabana Ramzan, and Waheed Anwar. "IOT-Based Cotton Whitefly Prediction Using Deep Learning." Scientific Programming 2021 (July 10, 2021): 1–17. http://dx.doi.org/10.1155/2021/8824601.

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Agriculture is suffering from the problem of low fertility and climate hazards such as increased pest attacks and diseases. Early prediction of pest attacks can be very helpful in improving productivity in agriculture. Insect pest (whitefly) attack has a high influence on cotton crop yield. Internet of Things solution is proposed to predict the whitefly attack to take prevention measures. An insect pest prediction system (IPPS) was developed with the help of the Internet of Things and a RBFN algorithm based on environmental parameters such as temperature, humidity, rainfall, and wind speed. Pest Warning and Quality Control of Pesticides proposed an economic threshold level for prediction of whitefly attack. The economic threshold level and RBFN algorithm are used to predict the whitefly attack using temperature, humidity, rainfall, and wind speed. The seven evaluation metrics accuracy, f-measures, precision, recall, Cohen’s kappa, ROC AUC, and confusion matrix are used to determine the performance of the RBFN algorithm. The proposed insect pest prediction system is deployed in the high influenced region of pest that provides pest prediction information to the farmer to take control measures.

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Cadenas, Jose M., M. Carmen Garrido, and Raquel Martinez-España. "Development of an application to make knowledge available to the farmer: Detection of the most suitable crops for a more sustainable agriculture." Journal of Ambient Intelligence and Smart Environments 12, no. 5 (September 21, 2020): 419–32. http://dx.doi.org/10.3233/ais-200575.

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Precision agriculture has different strategies to collect, process and analyze different types and nature data to be able to make decisions that improve the efficiency, productivity, quality, profitability and sustainability of agricultural production. Specifically, crop sustainability is directly related to reducing costs for farmers and minimizing environmental impact. In this paper, an application to help in the decision making about the most convenient type of crop to plant in a certain zone is developed, taking into account the climate conditions of that zone, in order to make a sustainable crop. This application is integrated within the Internet of Things system, which can be adapted and parameterized for any kind of crop and zone. The Internet of Things system components are described in detail and a fuzzy clustering model is proposed for the system’s intelligent module. This fuzzy model focuses on making a zone grouping (management zones), taking into account the zone climate conditions. The model manages fuzzy data, which allows us more extensive information and a more natural data treatment. A real study case of the proposed application is presented using data from the Region of Murcia (Spain). In this study case, the entire deployed Internet of Things system has been described, the fuzzy model to group similar areas in terms of meteorology has been validated and evaluated and the recommendation module has been implemented, taking into account the actual production data and the needed resources for the crops in the Region of Murcia (Spain).

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Tsouros, Dimosthenis C., Stamatia Bibi, and Panagiotis G. Sarigiannidis. "A Review on UAV-Based Applications for Precision Agriculture." Information 10, no. 11 (November 11, 2019): 349. http://dx.doi.org/10.3390/info10110349.

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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Zhang, Chunling, and Zunfeng Liu. "Application of big data technology in agricultural Internet of Things." International Journal of Distributed Sensor Networks 15, no. 10 (October 2019): 155014771988161. http://dx.doi.org/10.1177/1550147719881610.

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This article first analyzes big data technology. Then, the agricultural Internet of Things system was established, and the acquisition of agricultural data was achieved through the establishment of sensor modules, image acquisition modules, and meteorological acquisition modules. The data are transmitted to the server through GPRS communication technology and 3G network card to realize data transmission. The Web Service technology is used to connect the Internet of Things with the neural network model to achieve data interoperability. By comparing the prediction results and actual data of the model, it is found that the prediction error of the model designed in this article is less than 1%, and the high-precision prediction of agricultural data is realized, which provides an effective guidance for the improvement of agricultural product quality and yield.

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Lászlop, Ádám. "The role and limitations of the unmanned aerial vihicle in the precision small and middle-size crop production." Acta Periodica 21 (2020): 29–40. http://dx.doi.org/10.47273/ap.2020.21.29-40.

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Internet of Things (IoT) could prove significantly potential in precision farming, facilitating the acquisition of real-time crop’s, soil, and environmental data through the use of Unmanned Aerial Vehicles (UAVs) with their capabilities to capture high spatial and temporal resolution imagery. The effective deployment of such vehicles and linking them with on-farm sensors can transform agriculture that would allow it to move into the next era of agriculture. The Precise data collection can enable small and middle-size growers to make planting decisions well ahead of time and follow the cultural practices based on the model extracted from the data collected for the multiple factors from their farm locations. This data can significantly help farmers while making quick decisions, reducing agricultural inputs costs such as seeds, insecticides, and fertilizers, and can boost agricultural production. We will also look into different roles that UAVs can perform in precision farming and their limitations in line with recent EU Directives (EU) 2019/947 and (EU) 2019/945 on the rules and procedures for the operation of Unmanned Aircraft in the European Union air space. We will also review the entire range of precision farming practices that can help farmers taking full advantage of the available technology for automating farming practices that save their time and money with accuracy, effectively.

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Journal articles on the topic 'Internet of things, Precision agriculture'

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