TY - JOUR
T1 - Detection of Colchicum autumnale in drone images, using a machine-learning approach
AU - Petrich, Lukas
AU - Lohrmann, Georg
AU - Neumann, Matthias
AU - Martin, Fabio
AU - Frey, Andreas
AU - Stoll, Albert
AU - Schmidt, Volker
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020
Y1 - 2020
N2 - Colchicum autumnale are toxic autumn-blooming flowering plants, which often grow on extensive meadows and pastures. Thus, they pose a threat to farm animals especially in hay and silage. Intensive grassland management or the use of herbicides could reduce these weeds but environment protection requirements often prohibit these measures. For this reason, a non-chemical site- or plant-specific weed control is sought, which aims only at a small area around the C. autumnale and with low impact on the surrounding flora and fauna. For this purpose, however, the exact locations of the plants must be known. In the present paper, a procedure to locate blooming C. autumnale in high-resolution drone images in the visible light range is presented. This approach relies on convolutional neural networks to detect the flower positions. The training data, which is based on hand-labeled images, is further enhanced through image augmentation. The quality of the detection was evaluated in particular for grassland sites which were not included in the training to get an estimate for how well the detector works on previously unseen sites. In this case, 88.6% of the flowers in the test dataset were detected, which makes it suitable, e.g., for applications where the training is performed by the manufacturer of an automatic treatment tool and where the practitioners apply it to their previously unseen grassland sites.
AB - Colchicum autumnale are toxic autumn-blooming flowering plants, which often grow on extensive meadows and pastures. Thus, they pose a threat to farm animals especially in hay and silage. Intensive grassland management or the use of herbicides could reduce these weeds but environment protection requirements often prohibit these measures. For this reason, a non-chemical site- or plant-specific weed control is sought, which aims only at a small area around the C. autumnale and with low impact on the surrounding flora and fauna. For this purpose, however, the exact locations of the plants must be known. In the present paper, a procedure to locate blooming C. autumnale in high-resolution drone images in the visible light range is presented. This approach relies on convolutional neural networks to detect the flower positions. The training data, which is based on hand-labeled images, is further enhanced through image augmentation. The quality of the detection was evaluated in particular for grassland sites which were not included in the training to get an estimate for how well the detector works on previously unseen sites. In this case, 88.6% of the flowers in the test dataset were detected, which makes it suitable, e.g., for applications where the training is performed by the manufacturer of an automatic treatment tool and where the practitioners apply it to their previously unseen grassland sites.
KW - Colchicum autumnale
KW - Convolutional neural network
KW - Drone image
KW - Object detection
UR - http://www.scopus.com/inward/record.url?scp=85084441616&partnerID=8YFLogxK
U2 - 10.1007/s11119-020-09721-7
DO - 10.1007/s11119-020-09721-7
M3 - Article
SN - 1385-2256
VL - 21
SP - 1291
EP - 1303
JO - Precision Agriculture
JF - Precision Agriculture
IS - 6
ER -