Machine learning regression model for predicting honey harvests
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Honey yield from apiary sites varies significantly between years. This affects the beekeeper’s ability to manage hive health, as well as honey production. This also has implications for ecosystem services, such as forage availability for nect...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/81343 |
| _version_ | 1848764351669862400 |
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| author | Campbell, Tristan Dixon, Kingsley Dods, K. Fearns, Peter Handcock, Rebecca |
| author_facet | Campbell, Tristan Dixon, Kingsley Dods, K. Fearns, Peter Handcock, Rebecca |
| author_sort | Campbell, Tristan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Honey yield from apiary sites varies significantly between years. This affects the beekeeper’s ability to manage hive health, as well as honey production. This also has implications for ecosystem services, such as forage availability for nectarivores or seed sets. This study investigates whether machine learning methods can develop predictive harvest models of a key nectar source for honeybees, Corymbia calophylla (marri) trees from South West Australia, using data from weather stations and remotely sensed datasets. Honey harvest data, weather and vegetation-related datasets from satellite sensors were input features for machine learning algorithms. Regression trees were able to predict the marri honey harvested per hive to a Mean Average Error (MAE) of 10.3 kg. Reducing input features based on their relative model importance achieved a MAE of 11.7 kg using the November temperature as the sole input feature, two months before marri trees typically start to produce nectar. Combining weather and satellite data and machine learning has delivered a model that quantitatively predicts harvest potential per hive. This can be used by beekeepers to adaptively manage their apiary. This approach may be readily applied to other regions or forage species, or used for the assessment of some ecosystem services. |
| first_indexed | 2025-11-14T11:17:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-81343 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:17:59Z |
| publishDate | 2020 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-813432021-01-07T07:46:46Z Machine learning regression model for predicting honey harvests Campbell, Tristan Dixon, Kingsley Dods, K. Fearns, Peter Handcock, Rebecca Science & Technology Life Sciences & Biomedicine Agronomy Agriculture remote sensing weather Corymbia calophylla honey machine learning prediction MODIS PATTERNS CLIMATE WATER © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Honey yield from apiary sites varies significantly between years. This affects the beekeeper’s ability to manage hive health, as well as honey production. This also has implications for ecosystem services, such as forage availability for nectarivores or seed sets. This study investigates whether machine learning methods can develop predictive harvest models of a key nectar source for honeybees, Corymbia calophylla (marri) trees from South West Australia, using data from weather stations and remotely sensed datasets. Honey harvest data, weather and vegetation-related datasets from satellite sensors were input features for machine learning algorithms. Regression trees were able to predict the marri honey harvested per hive to a Mean Average Error (MAE) of 10.3 kg. Reducing input features based on their relative model importance achieved a MAE of 11.7 kg using the November temperature as the sole input feature, two months before marri trees typically start to produce nectar. Combining weather and satellite data and machine learning has delivered a model that quantitatively predicts harvest potential per hive. This can be used by beekeepers to adaptively manage their apiary. This approach may be readily applied to other regions or forage species, or used for the assessment of some ecosystem services. 2020 Journal Article http://hdl.handle.net/20.500.11937/81343 10.3390/agriculture10040118 English http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Agronomy Agriculture remote sensing weather Corymbia calophylla honey machine learning prediction MODIS PATTERNS CLIMATE WATER Campbell, Tristan Dixon, Kingsley Dods, K. Fearns, Peter Handcock, Rebecca Machine learning regression model for predicting honey harvests |
| title | Machine learning regression model for predicting honey harvests |
| title_full | Machine learning regression model for predicting honey harvests |
| title_fullStr | Machine learning regression model for predicting honey harvests |
| title_full_unstemmed | Machine learning regression model for predicting honey harvests |
| title_short | Machine learning regression model for predicting honey harvests |
| title_sort | machine learning regression model for predicting honey harvests |
| topic | Science & Technology Life Sciences & Biomedicine Agronomy Agriculture remote sensing weather Corymbia calophylla honey machine learning prediction MODIS PATTERNS CLIMATE WATER |
| url | http://hdl.handle.net/20.500.11937/81343 |