Efficient IoT system for Precision Agriculture

Suciu, George; Marcu, Ioana; Balaceanu, Cristina Mihaela; Dobrea, Marius; Botezat, Elena

doi:10.1109/emes.2019.8795102

Cited by 13 publications

(12 citation statements)

References 3 publications

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“…The more complex technologies associated with machines or equipment with embedded electronics, e.g., autopilot, telemetry, and applications at varying rates (8.7%), automated or robotic systems (6.5%) and digital maps, e.g., spatial variability of soil, relief, harvest monitor, vegetation indexes, and productivity (5.4%), with high potential for economic return, present a relatively low percentage use by the interviewed farmers in relation to the other technologies. The greater the soil spatial variation in the property and its influence on the application of inputs, the greater the potential economic return of the application at a variable rate in comparison with the uniform applications of the traditional system [12]. That is, the variable application of inputs depends on many factors; however, the authors commit that the variability inherent in an agricultural soil and the relative responsiveness of the yield to fertilizer inputs at different levels of concentration in that soil are the most important factors to influence the economic gain.…”

Section: Resultsmentioning

confidence: 99%

“…Internet and Connectivity/Wireless [14,36] Low Mobile APPs, Digital Platforms and Software [21,22] Global Positioning Systems [11] Digital Maps [12] Table 1. Cont.…”

Section: Reference Complexity Of Applicationsmentioning

confidence: 99%

“…Proximal and Field Sensors [5][6][7] Medium Remote Sensors [8][9][10] Embedded Electronics, Telemetry, and Automation [12,37] Deep Learning and Internet of Things [17][18][19][20] High Cloud Computing, Big Data, Blockchain and Cryptography [3,15,16] Artificial Intelligence [23] The questionnaire was prepared, organized, and made available to the public through the online platform LimeSurvey under the registration number 25889/2020 [38]. The system was available from 17 April to 2 June 2020, allowing answers to be collected based on specific questions previously defined ( Table 2).…”

Section: Reference Complexity Of Applicationsmentioning

confidence: 99%

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Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

et al. 2020

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The rapid population growth has driven the demand for more food, fiber, energy, and water, which is associated to an increase in the need to use natural resources in a more sustainable way. The use of precision agriculture machinery and equipment since the 1990s has provided important productive gains and maximized the use of agricultural inputs. The growing connectivity in the rural environment, in addition to its greater integration with data from sensor systems, remote sensors, equipment, and smartphones have paved the way for new concepts from the so-called Agriculture 4.0 or Digital Agriculture. This article presents the results of a survey carried out with 504 Brazilian farmers about the digital technologies in use, as well as current and future applications, perceived benefits, and challenges. The questionnaire was prepared, organized, and made available to the public through the online platform LimeSurvey and was available from 17 April to 2 June 2020. The primary data obtained for each question previously defined were consolidated and analyzed statistically. The results indicate that 84% of the interviewed farmers use at least one digital technology in their production system that differs according to technological complexity level. The main perceived benefit refers to the perception of increased productivity and the main challenges are the acquisition costs of machines, equipment, software, and connectivity. It is also noteworthy that 95% of farmers would like to learn more about new technologies to strengthen the agricultural development in their properties.

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Section: Resultsmentioning

confidence: 99%

“…Internet and Connectivity/Wireless [14,36] Low Mobile APPs, Digital Platforms and Software [21,22] Global Positioning Systems [11] Digital Maps [12] Table 1. Cont.…”

Section: Reference Complexity Of Applicationsmentioning

confidence: 99%

Section: Reference Complexity Of Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

et al. 2020

View full text Add to dashboard Cite

The rapid population growth has driven the demand for more food, fiber, energy, and water, which is associated to an increase in the need to use natural resources in a more sustainable way. The use of precision agriculture machinery and equipment since the 1990s has provided important productive gains and maximized the use of agricultural inputs. The growing connectivity in the rural environment, in addition to its greater integration with data from sensor systems, remote sensors, equipment, and smartphones have paved the way for new concepts from the so-called Agriculture 4.0 or Digital Agriculture. This article presents the results of a survey carried out with 504 Brazilian farmers about the digital technologies in use, as well as current and future applications, perceived benefits, and challenges. The questionnaire was prepared, organized, and made available to the public through the online platform LimeSurvey and was available from 17 April to 2 June 2020. The primary data obtained for each question previously defined were consolidated and analyzed statistically. The results indicate that 84% of the interviewed farmers use at least one digital technology in their production system that differs according to technological complexity level. The main perceived benefit refers to the perception of increased productivity and the main challenges are the acquisition costs of machines, equipment, software, and connectivity. It is also noteworthy that 95% of farmers would like to learn more about new technologies to strengthen the agricultural development in their properties.

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“…The digital agriculture can be understood and encompasses communication, information, and spatial analysis technologies that allow rural producers to plan, monitor, and manage the operational and strategic activities of the production system. In addition to the technologies already consolidated, such as field sensors [6,7] orbital remote sensors [8] and also embedded in UAV-Unmanned Aerial Vehicle global positioning systems telemetry and automation [9] digital maps-soil relief, production, productivity [9] digital agriculture also involves the Internet and connectivity in crops [10] cloud computing, big data, blockchain and cryptography [11,12] deep learning [13] Internet of Things (IoT) [14] mobile applications and digital platforms [15,16] and artificial intelligence [17]. All these technologies support pre and postproduction decisions and greater sustainability of production systems [18] in addition to access to a differentiated market benefiting short marketing chains.…”

Section: Introductionmentioning

confidence: 99%

Application of Digital Technologies for the Next Level of Agriculture Growth and Transformation under Changing the Indian Agriculture: A Critical Review

Naresh

Kumar

Chandra

et al. 2021

IJECC

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The rapid population growth has driven the demand for more food, fiber, energy, and water, which is associated to an increase in the need to use natural resources in a more sustainable way. The growing connectivity in the rural environment, in addition to its greater integration with data from sensor systems, remote sensors, equipment, and smart-phones have paved the way for new concepts from the so-called digital agriculture. The information that crops offer is turned into profitable decisions only when efficiently managed. Current advances in data management are making Smart Farming grow exponentially as data have become the key element in modern agriculture to help producers with critical decision-making. Valuable advantages appear with objective information acquired through sensors with the aim of maximizing productivity and sustainability. These kind of data-based managed farms rely on data that can increase efficiency by avoiding the misuse of resources and the pollution of the environment. Data-driven agriculture, with the help of robotic solutions incorporating artificial intelligent techniques, sets the grounds for the sustainable agriculture of the future. Digital agriculture offers far-reaching opportunities for accelerating agricultural transformation. Although there are concerns that digital agriculture will enhance the market power of large agribusiness enterprises and increase the digital divide, a combination of new actors and public action can help accelerate the supply of digital agricultural technology, manage threats of market concentration, and harness the opportunities of digital agriculture for all. The agriculture industry has radically transformed over the past 50 years. Advances in machinery have expanded the scale, speed, and productivity of farm equipment, leading to more efficient cultivation of more land. Seed, irrigation, and fertilizers also have vastly improved, helping farmers increase yields. Now, agriculture is in the early days of yet another revolution, at the heart of which lie data and connectivity. Artificial intelligence, analytics, connected sensors, and other emerging technologies could further increase yields, improve the efficiency of water and other inputs, and build sustainability and resilience across crop cultivation and animal husbandry. This paper reviews the current status of advanced farm management systems by revisiting each crucial step, from data acquisition in crop fields to variable rate applications, so that growers can make optimized decisions to save money while protecting the environment and transforming how food will be produced to sustainably match the forthcoming population growth.

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“…Internet of Things (IoT) technology enables sensors to collect data and has provided important technologies for a variety of intelligent systems. The precision agriculture system is one of the most 2 of 19 important IoT systems in recent years, by which higher productivity, sustainable profitability, and higher quality products can be achieved based on information technology [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Jin

Wang

et al. 2020

Sustainability

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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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Efficient IoT system for Precision Agriculture

Cited by 13 publications

References 3 publications

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

Application of Digital Technologies for the Next Level of Agriculture Growth and Transformation under Changing the Indian Agriculture: A Critical Review

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

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