Early prediction of bumblebee flight task using machine learning

•An accurate model to predict bumblebee errand within 6 measurements.•Best model chosen from Neural Network, Random Forest, and Support Vector Machine.•Final hit rate of over 90%.•Includes a filtering process to mitigate losses from radar technology.•Useful to support insect tracking systems, pollin...

Full description

Saved in:
Bibliographic Details
Published in:Computers and electronics in agriculture 2021-05, Vol.184, p.106065, Article 106065
Main Authors: Williams, S.M., Aldabashi, N., Palego, C., Woodgate, J.L., Makinson, J.C., Cross, P.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Bees
Bombus
Bumblebees
Decline
Entropy (Information theory)
Flowering
Harmonic radar
Insect tracking
Insects
Machine learning
Model accuracy
Neural networks
Pollinator
Position measurement
Support vector machines
Target acquisition
Tracking
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•An accurate model to predict bumblebee errand within 6 measurements.•Best model chosen from Neural Network, Random Forest, and Support Vector Machine.•Final hit rate of over 90%.•Includes a filtering process to mitigate losses from radar technology.•Useful to support insect tracking systems, pollination services, and nest monitoring. This work demonstrates the development of a neural network algorithm able to determine the function of a bee’s flight within six measurements (≈18 s with current radar technology) of its relative position on leaving a nest. Engineering advancements have created technology to track individual insects, unlocking research possibilities to investigate how bumblebees react to their environment in more detail. This includes how they discover and make use of resources. The development of an intelligent algorithm would allow for the automated monitoring of resource use and nest health. An imbalance of bee flight tasks may indicate a shortage of resources or over-reliance on a plant that may soon stop flowering. Recent developments using drones to track insects can benefit from an intelligent target acquisition system given limited drone battery life. Such knowledge will also benefit the tracking itself by allowing for customised flight parameters to match target flight patterns. Data captured by these tracking techniques are taxing to parse manually using human expertise. Artificial intelligence can produce meaningful knowledge faster with equal precision. In this work, a comparison between a neural network (NN), random forest (RF), and support vector machine (SVM) is provided to distinguish the best model for the task by comparing cross entropy loss and accuracy across the dataset, showing improved results as time goes on. In situations where the radar lost sight of the target, a purpose-built filter was created to mitigate signal losses. The generated model provides results with a peak accuracy of 92%. This model, combined with the filter, create an opportunity to monitor the number of bees leaving the nest for each flight task with smaller, cheaper, and stationary receiver solutions with shorter ranges by removing the need to track a bee for its entire flight to ascertain its errand.
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2021.106065