TY - JOUR
T1 - A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony
AU - Bjerge, Kim
AU - Frigaard, Carsten Eie
AU - Mikkelsen, Peter Høgh
AU - Nielsen, Thomas Holm
AU - Misbih, Michael
AU - Kryger, Per
PY - 2019/9
Y1 - 2019/9
N2 - This paper presents a portable computer vision system, that is able to monitor the infestation level of the Varroa destructor mite in a beehive by recording a video sequence of live honeybees Apis mellifera for 5–20 min. A video monitoring unit with multispectral illumination and camera was designed to be placed in front of the beehive, where bees from a selected frame were shaken off. Subsequently, a computer vision algorithm (denoted as the Infestation Level Estimator) based on deep learning analysis of the video stream counted the number of honeybees and found the position of identified varroa mites. In this paper, the design and the algorithm that were used to determine the number of bees and mites are presented. Based on a video sequence with 1775 bees and 98 visual mites, the algorithm measured the infestation level to 5.80% compared to a ground truth of 5.52%. The algorithm had a high F1-score accuracy for counting bees (0.97), while the F1-score for detecting varroa mites was lower (0.91). The latter was due to mispredictions, which can be resolved by improving both the trained varroa classifier and the mechanical setup. Overall, the proposed computer vision system and algorithm showed a promising results in nondestructive and automatic monitoring of infestation levels in honeybee colonies and should be considered as an alternative to traditional methods, which require the killing of bees.
AB - This paper presents a portable computer vision system, that is able to monitor the infestation level of the Varroa destructor mite in a beehive by recording a video sequence of live honeybees Apis mellifera for 5–20 min. A video monitoring unit with multispectral illumination and camera was designed to be placed in front of the beehive, where bees from a selected frame were shaken off. Subsequently, a computer vision algorithm (denoted as the Infestation Level Estimator) based on deep learning analysis of the video stream counted the number of honeybees and found the position of identified varroa mites. In this paper, the design and the algorithm that were used to determine the number of bees and mites are presented. Based on a video sequence with 1775 bees and 98 visual mites, the algorithm measured the infestation level to 5.80% compared to a ground truth of 5.52%. The algorithm had a high F1-score accuracy for counting bees (0.97), while the F1-score for detecting varroa mites was lower (0.91). The latter was due to mispredictions, which can be resolved by improving both the trained varroa classifier and the mechanical setup. Overall, the proposed computer vision system and algorithm showed a promising results in nondestructive and automatic monitoring of infestation levels in honeybee colonies and should be considered as an alternative to traditional methods, which require the killing of bees.
KW - Apis melifera
KW - Computer vision
KW - Deep learning
KW - Multi-spectral illumination
KW - Varroa destructor
UR - http://www.scopus.com/inward/record.url?scp=85069506250&partnerID=8YFLogxK
U2 - 10.1016/j.compag.2019.104898
DO - 10.1016/j.compag.2019.104898
M3 - Journal article
AN - SCOPUS:85069506250
SN - 0168-1699
VL - 164
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
IS - September
M1 - 104898
ER -