Detection of Colchicum autumnale in drone images, using a machine-learning approach
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Detection of Colchicum autumnale in drone images, using a machine‑learning approach Lukas Petrich1,2 · Georg Lohrmann2 · Matthias Neumann1 · Fabio Martin2 · Andreas Frey2 · Albert Stoll2 · Volker Schmidt1
© The Author(s) 2020
Abstract 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. Keywords Colchicum autumnale · Convolutional neural network · Drone image · Object detection
Introduction Extensive grassland sites are mowed usually once or twice a year and are not or little fertilized. The typical late mowing dates in the middle of June for these sites help the proliferation of Colchicum autumnale on damp and clay soils. A change in the grassland management is often not possible, because of environment protection requirements. It * Lukas Petrich lukas.petrich@uni‑ulm.de 1
Institute of Stochastics, Ulm University, 89069 Ulm, Germany
2
Hochschule für Wirtschaft Und Umwelt Nürtingen-Geislingen, Neckarsteige 6‑10, 72622 Nürtingen, Germany
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Vol.:(0123456789)
Precision Agriculture
excludes also the use of herbicides. This is a problem for farmers who want to utilize the grassland to feed their animals as all parts of C. autumnale are poisonous and can lead to respiratory paralysis and death. In pastures, animals usually avoid the plants. However, leaves and seed in hay and silage cannot be detected by the animals and have to be avoided. State of the art in control of C. autumnale is area-wide mulching of the overall site. The studies of Seither and Elsäßer (2014) showed that the above ground population could be reduced by mulching in April or May, at the time of maximum depleted reserve of the onion. However, area-wide mulching contradicts the required extensive
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