Next Article in Journal
Pathogens Associated to Kiwifruit Vine Decline in Italy
Previous Article in Journal
Assessing the Productivity of Common Bean in Intercrop with Maize across Agro-Ecological Zones of Smallholder Farms in the Northern Highlands of Tanzania
Open AccessArticle

Machine Learning Regression Model for Predicting Honey Harvests

Computing and Mathematical Sciences, School of Electrical Engineering, Curtin University, Perth 6102, Australia
School of Molecular and Life Sciences, Curtin University, Perth 6102, Australia
Chem Centre, Perth 6102, Australia
Curtin Institute for Computation, Curtin University, Perth 6102, Australia
Author to whom correspondence should be addressed.
Agriculture 2020, 10(4), 118;
Received: 9 February 2020 / Revised: 18 March 2020 / Accepted: 18 March 2020 / Published: 9 April 2020
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. View Full-Text
Keywords: remote sensing; weather; Corymbia calophylla; honey; machine learning; prediction remote sensing; weather; Corymbia calophylla; honey; machine learning; prediction
Show Figures

Figure 1

MDPI and ACS Style

Campbell, T.; Dixon, K.W.; Dods, K.; Fearns, P.; Handcock, R. Machine Learning Regression Model for Predicting Honey Harvests. Agriculture 2020, 10, 118.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop