Deep Learning-Driven Predictive Modelling for Optimizing Stingless Beekeeping Yields

Environmental factors like temperature, solar irradiance, and rain may influence the health and productivity of stingless bees. This paper aims to investigate the best approaches applied in meliponiculture to predict beehive health and products based on environmental variables and bee activity data...

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Bibliographic Details

Published in:	Journal of Computing Research and Innovation
Main Authors:	Noor Hafizah Khairul Anuar, Mohd Amri Md Yunus, Muhammad Ariff Baharudin, Sallehuddin Ibrahim, Shafishuhaza Sahlan
Format:	Article
Language:	English
Published:	Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis 2024-09-01
Subjects:	Melinponiculture deep learning LSTM RNN Stingless Beekeeping Yields Yields estimation
Online Access:	https://jcrinn.com/index.php/jcrinn/article/view/451