# Machine Learning Regression Model for Predicting Honey Harvestis
## 1 Introduction  
The beekeeping industry in Western Australia has grown rapidly in the past decade, from 660 registered beekeepers in 2010 to over 3000 in 2019 [1]. In addition, honey produced from Western Australia has some of the highest antimicrobial properties known for honey [2]. As these high antimicrobial honey varieties are produced from marri (_Corymbia calophyllla_, Myrtaceae) and jarrah (_Eucalyptus marginata_, Myrtaceae) trees that occur across a large area (see Figure 1) of approximately 84,000 km\({}^{2}\)[3], beekeepers often travel long distances to inspect apiary sites and manage their beehives. Access to a tool to predict areas of higher and lower honey production would make apiary management more efficient and improve industry safety by reducing the amount of rural driving required.  
Efforts to predict flowering patterns of Myrtaceous trees in Australia from weather data have often found complex relationships between weather and phenology. For example, a phenological study of four different species of _Eucalyptus_ with coincident geographic ranges analysed 400 data points of flowering status and climate over 30 years [4] with the Generalised Additive Model for Location, Scale and Shape (GAMLSS) technique to assess the impact of minimum, mean and maximum temperature for the flowering period of those species, as well as flowering intensity in the preceding months and season. This study found that there is a non-linear relationship between temperature and flowering intensity, both of which varied between the four species. Two species flowered more intensely with increasing maximum temperatures, one species flowered more intensely with increasing minimum temperature and one species flowered less intensely with increasing maximum temperature. While temperature was consistently a key factor in flowering intensity, the effect was far from consistent.  
While several studies on the relationship between satellite-derived vegetation indices and honey production in south-east Australia have shown promising outcomes [5; 6; 7], these have been qualitative in nature, with no demonstration of quantitative relationships, nor predictive model development.  
[PERSON] and [PERSON] [8] developed a qualitative, relativistic model for landscape/regional scale nectar availability in subtropical Eastern Australia, covering an area of 314,400 Ha. This study identified some key factors that were related to nectar availability, notably the Gross Primary Productivity for the previous 12 months and the average annual solar radiation and rainfall for the previous 6 months. The study was broad, covering more than 50 different plant species from many different genera.  
In this paper, we use machine learning regression methods to assess the capability of both weather data and satellite-derived vegetation and moisture related data to develop a honey harvest prediction model for marri honey. This includes an assessment of the spatial density of weather station data from the Australian Government's Bureau of Meteorology (BOM). The primary aim is to identify the key factors that influence marri honey production and identify potential limitations in the data sources for these key factors.  
Figure 1: Geographic extent of _Corymbia calophyllla_ (**a**) and _Eucalyptus marginata_ (**b**) [3].