Pattern analysis of daily beehive weight variation for colony health assessment

Abstract

Continuous monitoring of beehive conditions plays a crucial role in assessing colony health, improving productivity, and supporting effective beekeeping management. This study presents a data-driven framework for classifying daily hive weight variation patterns using a multi-sensor beehive monitoring system. Weight data were collected at 10-minute intervals and processed using piecewise linear regression to extract key features. Principal component analysis (PCA) and engineered features were used to reduce dimensionality and enhance interpretability. Six supervised learning models were evaluated using K-fold cross-validation. The Support Vector Classification (SVC) model achieved the highest performance in classifying daily weight variation patterns, with an F1-score of 0.905 using the nine most significant features, comprising seven weight points and two derived meaningful indicators − net daily weight change and harvested weight. The analysis revealed six common daily weight variation patterns, each reflecting distinct behavioral and environmental scenarios, such as active foraging, nectar scarcity, or post-feeding inactivity. Seasonal analyses further indicated clear relationships between these weight patterns and environmental conditions, confirming the method’s effectiveness in capturing colony behaviors and reflecting overall hive health. This study provides an effective and scalable approach for real-time hive health monitoring, contributing to the advancement of precision apiculture through the integration of smart sensing and machine learning.