Design and deployment of a practical IoT-based monitoring system for protected cultivations

Hernández-Morales, Carlos Alberto; Rivera, Juan Pablo; Pérez‐Jiménez, Rafael

doi:10.1016/j.comcom.2022.01.009

Cited by 40 publications

(11 citation statements)

References 35 publications

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“…Information systems and data transfer platforms from IoT devices for a farm management are discussed in the papers [28,[32][33][34][35][36][37] based on cloud technologies. Common requirements for the development of FMIS in agriculture are described in [32].…”

Section: Information Systems and Different Platforms Used For Farm Ma...mentioning

confidence: 99%

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Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Matsveichuk¹,

Sotskov²

2023

Preprint

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Development of agriculture in Russia and Belarus is based on the practical implementation of "smart" systems in agriculture based on the use of modern wireless, intelligent technologies and Internet of Things. This review presents research articles (mainly, in Russian) published in the period of 2013 – 2022 on the use of cloud technologies and Internet of Things for the development of agriculture in Russia and Belarus. An analysis of the use of cloud technologies and Internet of Things in the modern world is given on the basis of research articles and reviews published in English in the period of 2017 – 2022. The main directions of digitalization of modern agriculture are listed. The uses of cloud technologies and Internet of Things in agriculture are described along with promising directions for further research and applications.

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Section: Information Systems and Different Platforms Used For Farm Ma...mentioning

confidence: 99%

“…The article [33] developed a scalable IoT-based monitoring system with forecasting capabilities for agriculture. The proposed IoT system was designed and experimentally tested by monitoring a temperature and humidity in a commercial tomato greenhouse in Mexico for six months.…”

Section: Information Systems and Different Platforms Used For Farm Ma...mentioning

confidence: 99%

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Matsveichuk¹,

Sotskov²

2023

Preprint

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“…However, the use of many sensors is hampered by several factors related to connectivity. Commercial farms in developing countries and remote regions have inadequate access to the internet and infrastructure required to support intelligent machines; this explains why most innovations are concentrated in developed countries [ 66 , 67 , 68 , 69 , 70 , 71 , 72 ]. Wired sensors are less efficient than wireless sensors, which can automatically relay signals remotely.…”

Section: Biomimetic Innovations and Climate-smart Agriculturementioning

confidence: 99%

Bio-Inspired Robots and Structures toward Fostering the Modernization of Agriculture

et al. 2022

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Biomimetics is the interdisciplinary cooperation of biology and technology that offers solutions to practical problems by analyzing biological systems and transferring their principles into applications. This review article focused on biomimetic innovations, including bio-inspired soft robots and swarm robots that could serve multiple functions, including the harvesting of fruits, pest control, and crop management. The research demonstrated commercially available biomimetic innovations, including robot bees by Arugga AI Farming and the Robotriks Traction Unit (RTU) precision farming equipment. Additionally, soft robotic systems have made it possible to mitigate the risk of surface bruises, rupture, the crushing destruction of plant tissue, and plastic deformation in the harvesting of fruits with a soft rind such as apples, cherries, pears, stone fruits, kiwifruit, mandarins, cucumbers, peaches, and pome. Even though the smart farming technologies, which were developed to mimic nature, could help prevent climate change and enhance the intensification of agriculture, there are concerns about long-term ecological impact, cost, and their inability to complement natural processes such as pollination. Despite the problems, the market for bio-inspired technologies with potential agricultural applications to modernize farming and solve the abovementioned challenges has increased exponentially. Future research and development should lead to low-cost FEA robotic grippers and FEA-tendon-driven grippers for crop harvesting. In brief, soft robots and swarm robotics have immense potential in agriculture.

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“…Protected agriculture is a modern agricultural operation which provides the most suitable environment and conditions for crop growth, thereby obtaining high-quality and high-yield agricultural products [1]. Compared with traditional agriculture, protected agriculture has the advantages of a high level of automation and production without seasonal restrictions [2].…”

Section: Introductionmentioning

confidence: 99%

A Cucumber Photosynthetic Rate Prediction Model in Whole Growth Period with Time Parameters

et al. 2023

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Photosynthetic rate prediction models can provide guidance for crop photosynthetic process optimization, which has been widely used in the precise regulation of the protected environment. The photosynthetic capacity of crops continuously changes during their whole growth process. Previous studies on photosynthetic models mainly consider the interaction between a crop’s photosynthetic rate and its outer environmental conditions and have been able to predict a crop’s photosynthetic rate in a certain growth period. However, photosynthetic rate prediction models for whole growth periods have not been proposed yet. To solve this question, this paper introduces growing time into a variable set and proposes a method for building a cucumber photosynthetic rate prediction model of whole growth periods. First, the photosynthetic rate of cucumber leaves under different environmental conditions (light, temperature, and CO2 concentration) during the whole growth period was obtained through a multi-gradient nested test. With the environmental data and the cultivation time as the inputs, a photosynthetic rate prediction model was built using the Support Vector Regression algorithm. In order to obtain better modeling results, multiple kernel functions were used for pretraining, and the parameters of the Support Vector Regression algorithm were optimized based on multiple population genetic algorithms. Compared with a Back Propagation neural network and Non-linear Regression method, the Support Vector Regression model optimized had the highest accuracy, with the coefficient of determination of the test set was 0.998, and the average absolute error was 0.280 μmol·m−2·s−1, which provides a theoretical solution for the prediction of the cucumber photosynthetic rate during the whole growth period.

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Design and deployment of a practical IoT-based monitoring system for protected cultivations

Cited by 40 publications

References 35 publications

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Bio-Inspired Robots and Structures toward Fostering the Modernization of Agriculture

A Cucumber Photosynthetic Rate Prediction Model in Whole Growth Period with Time Parameters

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