Rainfall Before Flowering Predicts Honey Yields, Machine Learning Study Finds

Honey yields are notoriously difficult to forecast. Beekeepers must decide months in advance whether to invest in supplementary feeding, extra hive boxes, labor, and the costly transport of colonies, all without knowing whether the coming season will reward them with a bumper harvest or a disappointing one. Now, a team of researchers from Chile, Peru, Brazil, and Australia has built a machine learning workflow that classifies each beekeeping season as poor, moderate, or good using nothing more than freely available weather data, and they have released everything, including the raw data and code, so that other scientists can reproduce and extend the work.

The study, published in the journal Smart Agricultural Technology, addresses a persistent gap in the literature on honey yield prediction. Previous efforts have ranged from simple regression equations linking climate to harvests in the United Kingdom two decades ago, to fuzzy inference systems, radial basis function interpolation, and modern algorithms such as random forests and gradient boosting applied in Australia, Spain, Italy, and Turkey. Yet the authors found that relatively few such studies are indexed in major databases like Scopus and Web of Science, and many of them keep their datasets closed, making it impossible for other researchers to verify results, compare models fairly, or build on the findings. The new work is explicitly designed to break that pattern.

At the heart of the study is a dataset of 49 records of average honey yield per hive, drawn from 15 apiaries in southwest Australia between 2011 and 2018. The main nectar source in that region is the marri tree, Corymbia calophylla, which flowers around February. Because reliable yield records are scarce and often confidential, the researchers deliberately kept the predictor set simple: minimum and maximum temperatures and rainfall, downloaded from weather stations of the Australian Government’s Bureau of Meteorology located near each apiary. From these they engineered 45 climatic features, including monthly mean temperatures, monthly rainfall, counts of days above 40 degrees Celsius and below 25 degrees Celsius, each indexed by how many months before flowering the measurement was taken.

The team formulated the problem as a supervised multiclass classification task, mirroring the methodology of the seminal 2020 study on marri honey harvests so that results could be compared directly. Yields below 20 kilograms per hive were labeled a poor year, between 20 and 40 kilograms a moderate year, and above 40 kilograms a good year. Exploratory analysis showed that the climatic variables overlap heavily across the three classes, which rules out simple threshold rules and statistical models, and instead points toward machine learning methods capable of capturing complex, nonlinear relationships.

A central innovation of the paper is its layered approach to interpretability. The researchers applied three complementary techniques to understand which variables drive the predictions. First, they computed feature importance in a random forest model based on the mean decrease in impurity, which revealed that rainfall variables dominate: rainfall five, one, and eight months before flowering emerged as the three most influential features, followed by minimum temperatures three and eleven months before flowering. Second, they quantified how frequently each variable appears at different depths of the decision trees inside the random forest, on the premise that the most relevant features sit closer to the root. This analysis largely corroborated the importance rankings, with rainfall eight and five months before flowering again standing out, while the count of extremely hot days above 40 degrees Celsius had almost no influence.

Third, the team used SHAP, or Shapley Additive Explanations, a method rooted in cooperative game theory that distributes the prediction among the input features according to their average marginal contribution. The SHAP analysis added class-specific nuance. For poor harvests, the minimum temperature eleven months before flowering was among the strongest explanatory variables, with low values pushing predictions away from that class. For moderate harvests, rainfall seven months before flowering carried the greatest weight, with high values contributing positively to that prediction. For good harvests, high values of rainfall eight and five months before flowering were the clearest signals. Taken together, the interpretability analyses consistently point to rainfall in the months leading up to flowering as the most reliable indicator of a strong honey season.

On the modeling side, the researchers benchmarked nine widely used algorithms: logistic regression, k-nearest neighbors, support vector machines, decision trees, multilayer perceptrons, random forests, linear discriminant analysis, gradient boosting, and Naive Bayes. They evaluated each under a battery of experimental conditions, including with and without feature selection, with four normalization schemes (no normalization, division by the maximum, min-max scaling, and z-score standardization), and under two cross-validation strategies, leave-one-out and stratified 10-fold. The effects were algorithm-dependent. Distance-based and margin-based methods such as k-nearest neighbors, support vector machines, logistic regression, and multilayer perceptrons benefited from min-max and z-score scaling, while tree-based models like random forests and decision trees were indifferent to normalization, and Naive Bayes sometimes performed worse when the data were rescaled.

Feature selection based on the interpretability results, using the nine most important variables, improved several models, most dramatically linear discriminant analysis, which jumped from an accuracy of 0.65 to 0.80 under both validation schemes. A k-nearest neighbors model with k set to three also reached roughly 0.80 accuracy. To squeeze out further gains on the small dataset, the team applied SMOTE, a synthetic minority over-sampling technique that balances the classes during training, along with bagging and a Voting Classifier that combines the probability outputs of multiple base models. The best configuration paired k-nearest neighbors with a support vector machine under soft voting, achieving an accuracy of 0.82 and a Matthews correlation coefficient above 0.71. That figure represents a substantial improvement over the 0.67 accuracy reported in the foundational Australian study, achieved here without any satellite or remote sensing data at all.

The authors are careful about what these numbers mean. With only 49 samples and no independent external test set, the reported performance reflects cross-validation estimates rather than proven generalization to new regions or production systems. Hyperparameter searches proved unstable on such a small dataset, so the team deliberately used default settings to keep the comparison controlled and reproducible. They frame the contribution not as a single performance record but as a replicable workflow: the raw climatic data, the yield database, the preprocessing scripts, the feature selection procedure, and the model evaluations are all openly available on GitHub, so that any research group can rerun the pipeline, adapt it to local flowering calendars, and retrain the models on regional data.

The practical implications reach beyond the laboratory. Because the model classifies seasons as low, moderate, or high yield potential using weather records that are essentially free, it could power a simple web or mobile tool in which a beekeeper selects an apiary location, the system links it to the nearest weather station, and the season’s outlook appears as an early alert. Such a signal could guide decisions on feeding schedules, which follow Farrar’s rule that a hive’s honey production capacity grows exponentially with bee population, as well as the preparation of honey supers, transhumance planning, labor allocation, and harvest logistics. Cooperatives, technical advisors, and public institutions could use territory-scale forecasts to plan assistance programs and climate adaptation strategies. The authors stress that the tool is meant to support, not replace, the beekeeper’s expert judgment, and that validation on new seasons and regions, along with the eventual addition of satellite vegetation data and hive sensor information, remains the necessary next step before the approach can be deployed at scale.

Subject of Research: Machine learning classification of honey yield per hive using climatic variables

Article Title: Machine learning models combined with feature importance methods for honey yield classification: A replicable approach

Article References: Ahumada-García, R., Zabala-Blanco, D., Monzón, V. H., Sánchez, I., da Silva, N. F. F., Rosa, T. C., Ferreira, A. I. S., López-Cortés, X., Flores-Calero, M., & Vasquez-Iglesias, P. (2026). Machine learning models combined with feature importance methods for honey yield classification: A replicable approach. Smart Agricultural Technology, 15, Article 102575. https://doi.org/10.1016/j.atech.2026.102575

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102575

Keywords: machine learning, honey yield prediction, beekeeping, random forest, SHAP explainability, feature importance, climate variables, rainfall, SMOTE, cross-validation, precision apiculture, reproducible research