Detection of Colchicum autumnale in drone images, using a machine-learning approach

Autor: Andreas Frey, Matthias Neumann, Lukas Petrich, Georg Lohrmann, Albert Stoll, Volker Schmidt, Fabio Martin
Rok vydání: 2020
Předmět:
Zdroj: Precision Agriculture. 21:1291-1303
ISSN: 1573-1618
1385-2256
DOI: 10.1007/s11119-020-09721-7
Popis: 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.
Databáze: OpenAIRE
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