TY - JOUR

T1 - A comparison of machine-learning assisted optical and thermal camera systems for beehive activity counting

AU - Morton Williams, Samuel

AU - Bariselli, Sara

AU - Palego, Cristiano

AU - Holland, Richard

AU - Cross, Paul

PY - 2022/12/1

Y1 - 2022/12/1

N2 - There is a documented shortage of reliable counting systems for the entrance of beehives. Movement at the entrance of a hive is a measure of hive health and abnormalities, in addition to an indicator of predators. To that end, two camera systems have been designed to provide a comparative analysis for a thermal camera system. The first, a visible spectrum camera, competed directly with the thermal camera. Machine learning is used to address the narrower field of view of the thermal camera, in addition to lost extracted tracks from both cameras. K-nearest-neighbour, support vector machine, random forest, and neural networks are used to classify flights as arriving, departing, or hovering bees. A hierarchical system is used to determine the nature of any flights where a clear label is not feasibly assigned based on the information from either test camera. A third camera at distance from the hive served as the end authority. After three iterations of training and validating, a test case is evaluated between both camera systems. Results from the test are compared to those from a human observer, showing that the thermal camera can perform with the same success as the visual camera despite a smaller field of view, fewer pixels and lower frame-rate, while both systems achieve greater than 96% accuracy and both camera systems are 93% successful at extracting flights. This is advantageous as a thermal camera will work in a wider range of environments, keeping the accuracy of an optical camera, and predicting based on movement characteristics will allow expanded uses such as predicting the presence of predators.

AB - There is a documented shortage of reliable counting systems for the entrance of beehives. Movement at the entrance of a hive is a measure of hive health and abnormalities, in addition to an indicator of predators. To that end, two camera systems have been designed to provide a comparative analysis for a thermal camera system. The first, a visible spectrum camera, competed directly with the thermal camera. Machine learning is used to address the narrower field of view of the thermal camera, in addition to lost extracted tracks from both cameras. K-nearest-neighbour, support vector machine, random forest, and neural networks are used to classify flights as arriving, departing, or hovering bees. A hierarchical system is used to determine the nature of any flights where a clear label is not feasibly assigned based on the information from either test camera. A third camera at distance from the hive served as the end authority. After three iterations of training and validating, a test case is evaluated between both camera systems. Results from the test are compared to those from a human observer, showing that the thermal camera can perform with the same success as the visual camera despite a smaller field of view, fewer pixels and lower frame-rate, while both systems achieve greater than 96% accuracy and both camera systems are 93% successful at extracting flights. This is advantageous as a thermal camera will work in a wider range of environments, keeping the accuracy of an optical camera, and predicting based on movement characteristics will allow expanded uses such as predicting the presence of predators.

KW - Thermal camera, Optical camera, Machine learning, Insect tracking, Apis mellifera

U2 - 10.1016/j.atech.2022.100038

DO - 10.1016/j.atech.2022.100038

M3 - Article

VL - 2

JO - Smart Agricultural Technology

JF - Smart Agricultural Technology

SN - 2772-3755

M1 - 100038

ER -