System design for inferring colony-level pollination activity through miniature bee-mounted sensors

Abstract In digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we pre...

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Main Authors: Haron M. Abdel-Raziq, Daniel M. Palmer, Phoebe A. Koenig, Alyosha C. Molnar, Kirstin H. Petersen
Format: Article
Language:English
Published: Nature Publishing Group 2021-02-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-82537-1
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spelling doaj-37aeb7b418dc4529a078e2a194dbafc02021-02-21T12:30:57ZengNature Publishing GroupScientific Reports2045-23222021-02-0111111210.1038/s41598-021-82537-1System design for inferring colony-level pollination activity through miniature bee-mounted sensorsHaron M. Abdel-Raziq0Daniel M. Palmer1Phoebe A. Koenig2Alyosha C. Molnar3Kirstin H. Petersen4Department of Electrical and Computer Engineering, Cornell UniversityDepartment of Electrical and Computer Engineering, Cornell UniversityDepartment of Electrical and Computer Engineering, Cornell UniversityDepartment of Electrical and Computer Engineering, Cornell UniversityDepartment of Electrical and Computer Engineering, Cornell UniversityAbstract In digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems.https://doi.org/10.1038/s41598-021-82537-1
collection DOAJ
language English
format Article
sources DOAJ
author Haron M. Abdel-Raziq
Daniel M. Palmer
Phoebe A. Koenig
Alyosha C. Molnar
Kirstin H. Petersen
spellingShingle Haron M. Abdel-Raziq
Daniel M. Palmer
Phoebe A. Koenig
Alyosha C. Molnar
Kirstin H. Petersen
System design for inferring colony-level pollination activity through miniature bee-mounted sensors
Scientific Reports
author_facet Haron M. Abdel-Raziq
Daniel M. Palmer
Phoebe A. Koenig
Alyosha C. Molnar
Kirstin H. Petersen
author_sort Haron M. Abdel-Raziq
title System design for inferring colony-level pollination activity through miniature bee-mounted sensors
title_short System design for inferring colony-level pollination activity through miniature bee-mounted sensors
title_full System design for inferring colony-level pollination activity through miniature bee-mounted sensors
title_fullStr System design for inferring colony-level pollination activity through miniature bee-mounted sensors
title_full_unstemmed System design for inferring colony-level pollination activity through miniature bee-mounted sensors
title_sort system design for inferring colony-level pollination activity through miniature bee-mounted sensors
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2021-02-01
description Abstract In digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems.
url https://doi.org/10.1038/s41598-021-82537-1
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