Accuracy vs. Energy: An Assessment of Bee Object Inference in Videos From On-Hive Video Loggers With YOLOv3, YOLOv4-Tiny, and YOLOv7-Tiny

A continuing trend in precision apiculture is to use computer vision methods to quantify characteristics of bee traffic in managed colonies at the hive\u27s entrance. Since traffic at the hive\u27s entrance is a contributing factor to the hive\u27s productivity and health, we assessed the potential of three open-source convolutional network models, YOLOv3, YOLOv4-tiny, and YOLOv7-tiny, to quantify omnidirectional traffic in videos from on-hive video loggers on regular, unmodified one- and two-super Langstroth hives and compared their accuracies, energy efficacies, and operational energy footprints. We trained and tested the models with a 70/30 split on a dataset of 23,173 flying bees manually labeled in 5819 images from 10 randomly selected videos and manually evaluated the trained models on 3600 images from 120 randomly selected videos from different apiaries, years, and queen races. We designed a new energy efficacy metric as a ratio of performance units per energy unit required to make a model operational in a continuous hive monitoring data pipeline. In terms of accuracy, YOLOv3 was first, YOLOv7-tiny—second, and YOLOv4-tiny—third. All models underestimated the true amount of traffic due to false negatives. YOLOv3 was the only model with no false positives, but had the lowest energy efficacy and highest operational energy footprint in a deployed hive monitoring data pipeline. YOLOv7-tiny had the highest energy efficacy and the lowest operational energy footprint in the same pipeline. Consequently, YOLOv7-tiny is a model worth considering for training on larger bee datasets if a primary objective is the discovery of non-invasive computer vision models of traffic quantification with higher energy efficacies and lower operational energy footprints

Ambient Electromagnetic Radiation as a Predictor of Honey Bee (\u3ci\u3eApis mellifera\u3c/i\u3e) Traffic in Linear and Non-Linear Regression: Numerical Stability, Physical Time and Energy Efficiency

Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, Utah, U.S.A. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 linear and 3,703,200 non-linear (random forest and support vector machine) regressors to predict bee motion counts from time, weather, and electromagnetic radiation. In all regressors, electromagnetic radiation was as good a predictor of traffic as weather. Both weather and electromagnetic radiation were better predictors than time. On the 13,412 time-aligned weather, electromagnetic radiation, and bee traffic records, random forest regressors had higher maximum R2 scores and resulted in more energy efficient parameterized grid searches. Both types of regressors were numerically stable

Classification of Motion Regions with Convolutional Networks, Support Vector Machines, and Random Forests in Video-Based Analysis of Bee Traffic

Bee traffic is the number of bees moving in a given area in front of a specific hive over a given period of time. Video-based bee traffic analysis has the potential to automate the assessment of bee traffic levels, which, in turn, may lead to the automation of honeybee colony health assessment. In this paper, we evaluate several convolutional networks to classify regions of detected motion as BEE or NO-BEE in videos captured by BeePi, an electronic beehive monitoring system. We compare the performance of several convolutional neural networks with the performance of support vector machines and random forests on the same image datase

Classification of Motion Regions with Convolutional Networks, Support Vector Machines, and Random Forests in Video-Based Analysis of Bee Traffic

Bee traffic is the number of bees moving in a given area in front of a specific hive over a given period of time. Video-based bee traffic analysis has the potential to automate the assessment of bee traffic levels, which, in turn, may lead to the automation of honeybee colony health assessment. In this paper, we evaluate several convolutional networks to classify regions of detected motion as BEE or NO-BEE in videos captured by BeePi, an electronic beehive monitoring system. We compare the performance of several convolutional neural networks with the performance of support vector machines and random forests on the same image datase