International Journal of Electrical and Computer Engineering (IJECE) Vol. 14, No. 2, April 2024, pp. 2282~2292 ISSN: 2088-8708, DOI: 10.11591/ijece.v14i2.pp2282-2292  2282 Journal homepage: http://ijece.iaescore.com A review on internet of things-based stingless bee's honey production with image detection framework Suziyani Rohafauzi 1 , Murizah Kassim 2,3 , Hajar Ja’afar 1 , Ilham Rustam 1 , Mohamad Taib Miskon 1 1 School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Terengganu, Malaysia 2 School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia 3 Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia Article Info ABSTRACT Article history: Received Jun 9, 2023 Revised Aug 3, 2023 Accepted Dec 5, 2023 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. Keywords: Convolution neural network Honey production Image detection Internet of things Stingless bee This is an open access article under the CC BY-SA license. Corresponding Author: Murizah Kassim Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA 40450 Shah Alam, Selangor, Malaysia Email: murizah@uitm.edu.my 1. INTRODUCTION Modern agriculture is recognized as one of the world's most important economic pillars, providing for an essential human need despite several challenges, such as the rising demand for agricultural products [1]. One agricultural subsector where new technology might boost productivity, reduce production costs, and simplify the operations of rearing and breeding bee colonies is beekeeping [2]. To achieve its objective of developing into a high-income nation, Malaysia requires new high-income initiatives. One such potential project is the ruthless beekeeping industry's contribution to agriculture and society. Food, medicine, diet cosmetics, and other related applications are the main industries that employ the stingless bee product. Although Malaysia has discovered over 38 species of stingless bees, only four are widely used for farming: Heterotrigona, Geniotrigona thoracica, Heterotrigona itama, Lepidotrigona terminata, and Tetragonula leviceps [3]. On the other hand, are two species that are particularly active in the production of honey and