Deep Learning-Driven Predictive Modelling for Optimizing Stingless Beekeeping Yields

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...

Full description

Saved in:
Bibliographic Details
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
Series:Journal of Computing Research and Innovation
Subjects:
Melinponiculture
deep learning
LSTM
RNN
Stingless Beekeeping Yields
Yields estimation
Online Access:https://www.conferencehunter.com/index.php/jcrinn/article/view/451
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.conferencehunter.com/index.php/jcrinn/article/view/451

Similar Items