A Lora-based Testbed Development for Stingless Bee Monitoring System

Miskon, Mohamad Taib; Razak, Muhd Azri Abdul; Ja’afar, Hajar; Hassan, N.S.; Mahmood, Rosman

doi:10.18421/tem112-26

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…If the bees are positioned near their food source, a healthy stingless bee colony can be obtained. A lack of food may lead the bee colony to starve [11]. Certain steps must be taken to prevent the conditions that led to this predicament.…”

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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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confidence: 99%