Makine Öğrenmesi ile Hava Durumu ve Arıcılık Verilerini Kullanarak Bal Arısı Koloni Sağlığının Tahmini

Abstract

Bal arısı kolonileri, küresel gıda güvenliği için hayati öneme sahiptir; ancak biyolojik ve çevresel stres faktörlerinin etkileşimi nedeniyle yüksek kayıplar yaşamaya devam etmektedir. Bu nedenle koloni sağlığının tahmin edilmesi, sürdürülebilir arıcılık yönetimi açısından öncelikli bir konudur. Bu çalışma, hava durumu ve mevsimsel değişkenlerle birlikte Sağlıklı Koloni Kontrol Listesi saha değerlendirmelerinden yararlanarak bal arısı koloni sağlığının öngörülebilirliğini incelemektedir. Kuzey Carolina ve Utah’taki arılıklardan 1.277 denetim ve yakın meteoroloji istasyonu kayıtları kullanılmıştır. Türetilen özellikler arasında buhar basıncı açığı, sıcaklık–nem etkileşimleri, rüzgâr enerjisi tahminleri ve mevsimsel kodlamalar bulunmaktadır. Tahmin görevi, ikili sınıflandırma (sağlıklı vs. sağlıksız) olarak kurgulanmıştır. Birçok makine öğrenmesi modeli test edilmiş, özellikle Rastgele Orman, Aşırı Rastgele Ağaçlar, Hafif Gradyan Artırma Makinesi, Kategorik Artırma, Gradyan Artırma, Histogram Tabanlı Gradyan Artırma ve Aşırı Gradyan Artırma gibi ağaç tabanlı topluluk yöntemleri üzerinde durulmuştur. Optimize edilmiş yumuşak oylama ve yığınlama gibi topluluk stratejileri de uygulanmıştır. Sonuçlar, doğruluk oranlarının %75–76 aralığında, ROC AUC değerlerinin ise 0,80’e yakın olduğunu göstermiştir. Hassasiyet %0,70’in üzerinde gerçekleşirken, duyarlılık düşük kalmıştır (~0,55). Mevsimsellik en baskın belirleyici olurken, yağış ve nem gibi hava durumu göstergeleri ek katkı sağlamıştır. Bulgular, tarımsal meteorolojik verilerin karar destek sistemlerinde yararlı olduğunu, ancak biyolojik ve yönetimsel değişkenlerin gelişmiş yöntemlerle bütünleştirilmesinin gerektiğini ortaya koymaktadır.

Keywords

• Bal arısı sağlığı
• sağlıklı koloni kontrol listesi
• makine öğrenmesi
• topluluk yöntemleri

Predicting Honeybee Colony Health Using Weather and Apiary Data with Machine Learning

Abstract

Honey bee colonies are essential for global food security but continue to suffer heavy losses from interacting biological and environmental stressors. Predicting colony health is therefore a priority for sustainable apicultural management. This study examines the feasibility of forecasting honey bee colony health using weather and seasonal variables together with field assessments from the Healthy Colony Checklist. A dataset of 1,277 inspections from apiaries in North Carolina and Utah, integrated with meteorological records from nearby stations, was analyzed. Engineered features included vapor pressure deficit, temperature–humidity interactions, wind energy estimates, and seasonal encodings. The prediction was structured as a binary classification task (healthy vs. unhealthy). Several machine learning models were tested, emphasizing tree-based ensembles such as Random Forest, Extra Trees, Light Gradient Boosting Machine, Categorical Boosting, Gradient Boosting, Histogram-based Gradient Boosting, and Extreme Gradient Boosting. Ensemble strategies, including optimized soft voting and stacking, were also applied. Results showed accuracies of 75–76% with ROC AUC values near 0.80. Precision exceeded 0.70, while recall remained modest (~0.55). Seasonality was the dominant predictor, with weather indicators providing complementary value. Findings confirm the usefulness of agrometeorological data for decision-support in apiculture but also highlight the limits of weather-only models. Incorporating hive-level biological and management factors with advanced learning methods is recommended.

Keywords

• Honey bee health
• healthy colony checklist
• machine learning
• ensemble methods

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