Internet of Things-Based Devices/Robots in Agriculture 4.0

Singh, Gulbir; Yogi, Kuldeep Kumar

doi:10.1007/978-981-16-6605-6_6

Cited by 16 publications

(4 citation statements)

References 23 publications

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“…Several studies have explored the use of IoT technologies in smart agriculture, and general reviews about the different contributions, challenges, and research directions in this field have been proposed by [ 5 , 6 , 20 , 21 , 36 , 37 ]. Furthermore, many applications of IoT in the agricultural field can be found in the literature ranging from controlling tasks like irrigation [ 38 ], fertilization [ 39 ], harvesting [ 10 ], and other operations [ 8 , 24 ] to plant growth monitoring tasks [ 40 , 41 ], farm management systems [ 42 , 43 ], indoor farming systems [ 44 , 45 , 46 , 47 ], and the management of the food production chain [ 48 ]. The widespread use of IoT in agriculture is a result of its ability in data handling, its easy installation, and its decreasing cost.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, numerous companies are developing groundbreaking products and services that exploit recent technologies; when these improve in terms of accessibility and affordability, their adoption in the agricultural sector will increase [ 2 ]. These technologies comprise internet of things (IoT) [ 3 , 4 , 5 , 6 ], robotics [ 7 , 8 ], artificial intelligence (AI) [ 9 , 10 ], blockchain [ 11 ], remote sensing technologies [ 12 , 13 , 14 ], image processing [ 15 , 16 ], and vertical farming [ 17 , 18 ]. The objectives are to automate the production process and build decision support systems [ 19 ] that use sensors to collect data on environmental conditions, soil condition, and plant growth, in addition to devices that control irrigation, fertilization, lighting, and other tasks.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture

Hadj Abdelkader,

Bouzebiba,

Pena

et al. 2023

Sensors

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Indoor agriculture is emerging as a promising approach for increasing the efficiency and sustainability of agri-food production processes. It is currently evolving from a small-scale horticultural practice to a large-scale industry as a response to the increasing demand. This led to the appearance of plant factories where agri-food production is automated and continuous and the plant environment is fully controlled. While plant factories improve the productivity and sustainability of the process, they suffer from high energy consumption and the difficulty of providing the ideal environment for plants. As a small step to address these limitations, in this article we propose to use internet of things (IoT) technologies and automatic control algorithms to construct an energy-efficient remote control architecture for grow lights monitoring in indoor farming. The proposed architecture consists of using a master–slave device configuration in which the slave devices are used to control the local light conditions in growth chambers while the master device is used to monitor the plant factory through wireless communication with the slave devices. The devices all together make a 6LoWPAN network in which the RPL protocol is used to manage data transfer. This allows for the precise and centralized control of the growth conditions and the real-time monitoring of plants. The proposed control architecture can be associated with a decision support system to improve yields and quality at low costs. The developed method is evaluated in emulation software (Contiki-NG v4.7),its scalability to the case of large-scale production facilities is tested, and the obtained results are presented and discussed. The proposed approach is promising in dealing with control, cost, and scalability issues and can contribute to making smart indoor agriculture more effective and sustainable.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture

Hadj Abdelkader,

Bouzebiba,

Pena

et al. 2023

Sensors

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“…Additionally, Kameoka et al described a method to apply optically sensed bioinformation to the cultivation process [22]. Furthermore, it was suggested that these sensing methods could have a close affinity with information technologies [23]. We have also been conducting research on multiband optical sensing to comprehensively acquire bioinformation relating to the elemental balance, organic components, and color appearance changes formed by the overall metabolic process [13,24]; we experimentally demonstrated the mid-infrared (MIR) spectroscopic determination of the nitrate nitrogen in fresh leaves [25].…”

Section: Introductionmentioning

confidence: 99%

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement

Hashimoto,

Suehara,

Kameoka

2024

Sensors

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By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein–nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites.

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“…However, particular challenges need to be met to allow the uptake of innovative technologies in smart farming to reach its promise [43]. The main challenge when combining innovative technologies in precision agriculture is the standardizing technology and the data synchronization of the involved assets.…”

Section: Introductionmentioning

confidence: 99%

End-to-end Precision Agriculture UAV-Based Functionalities Tailored to Field Characteristics

Raptis

Krestenitis

Egglezos

et al. 2023

J Intell Robot Syst

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This paper presents a novel, low-cost, user-friendly Precision Agriculture platform that attempts to alleviate the drawbacks of limited battery life by carefully designing missions tailored to each field’s specific, time-changing characteristics. The proposed system is capable of designing coverage missions for any type of UAV, integrating field characteristics into the resulting trajectory, such as irregular field shape and obstacles. The collected images are automatically processed to create detailed orthomosaics of the field and extract the corresponding vegetation indices. A novel mechanism is then introduced that automatically extracts possible problematic areas of the field and subsequently designs a follow-up UAV mission to acquire extra information on these regions. The toolchain is finished by using a deep learning module that was made just for finding weeds in the close-examination flight. For the development of such a deep-learning module, a new weed dataset from the UAV’s perspective, which is publicly available for download, was collected and annotated. All the above functionalities are enclosed in an open-source, end-to-end platform, named Cognitional Operations of micro Flying vehicles (CoFly). The effectiveness of the proposed system was tested and validated with extensive experimentation in agricultural fields with cotton in Larissa, Greece during two different crop sessions.

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Internet of Things-Based Devices/Robots in Agriculture 4.0

Cited by 16 publications

References 23 publications

Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture

Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement

End-to-end Precision Agriculture UAV-Based Functionalities Tailored to Field Characteristics

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