A novel autonomous remote system applied in agriculture using transmission control protocol

Deroussi, Anass; Madi, Abdessalam Aït; Alihamidi, Imam; Charaf, Lalla Amina; Saber, Mohammed; Erraoui, Anass; Addaim, Adnane

doi:10.11591/ijres.v11.i1.pp1-12

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Water is one of the most important intakes for plants and it is collected by the roots. Efforts have been made to improve the control for water supply of the conventional farming system by implementing IoT and automation technologies [17], [18]. It is important to note that in conventional and hydroponic farming, the roots have direct access to water since they are placed in soil, water, or other aggregates containing an adequate amount of water or humidity.…”

Section: Research Methods 21 Key Aeroponic Parametersmentioning

confidence: 99%

Internet of things based automated monitoring for indoor aeroponic system

Roffi

Jamhari

2023

IJECE

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On the ever-rising urgency of global food security, efforts are required to develop a robust farming technique. This includes the capability of farming in non-agricultural land or indoor spaces. Farming in the air medium, i.e., aeroponic, has persistently stepped in as a viable solution. Aeroponic farming allows efficient water usage while preventing soil related diseases and pests. With the assistance of light emitting diodes (LEDs) and precise electronic monitoring and control, aeroponic may become the suitable farming technique of the future. This work presents an aeroponic system capable of automated monitoring and control of farming parameters. The system achieved both robustness in indoor farming and remote access by employing LED as an artificial lighting system and the internet-of-things (IoT) connectivity, respectively. The test result demonstrated that the system successfully maintained the root chamber temperature below 30 °C with a typical average temperature of 28.8 °C. The system managed a humidity level which prevented plants from drying out. It was also evident that the LED assistance significantly improved the growth quality of <em>Ipomea reptans</em>. The system, data, and analysis presented in this work is expected to facilitate further development of a robust food production system in overcoming the global food crisis.

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Section: Research Methods 21 Key Aeroponic Parametersmentioning

confidence: 99%

Internet of things based automated monitoring for indoor aeroponic system

Roffi

Jamhari

2023

IJECE

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“…IoT is broadly used in many fields of smart agriculture [28]. There are many existing implementations of computerized identification on irrigation systems [29], pest detection [30], butterflies [31], crop production [32], varroa mites [33], and so on. However, the identification of the stingless bee relies very much on manual inspection by visually inspecting the hives and investigating their surroundings for possible problems.…”

Section: Iot Monitoring Systemmentioning

confidence: 99%

A review on internet of things-based stingless bee's honey production with image detection framework

Rohafauzi,

Kassim,

Ja’afar

et al. 2024

IJECE

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Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.

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