Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision

Autor/in: Günder, M., F. Ispizua, J. Kierdorf, R. Roscher, A.-K. Mahlein, C. Bauckhage
Jahr: 2022
Zeitschrift: GigaScience, Volume 11
Seite/n: DOI 10.1093/gigascience/giac054
Stichworte: UAV imaging · remote sensing · plant identiﬁcation · plant individualization · precision agriculture

Gefunden in Abteilung Sensorik & Datenanalyse

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Abstract

UAV-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the Observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. In this work, we present a hands-on workﬂow for the automatized temporal and spatial identiﬁcation and individualization of crop images from UAVs abbreviated as “cataloging” based on comprehensible computer vision methods. We evaluate the workﬂow on two real-world datasets. One dataset is recorded for observation of Cercospora leaf spot—a fungal disease—in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliﬂower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers large-scale spatio-temporal image dataset that in turn can be applied to train further machine learning models including various data layers. The presented approach improves analysis and interpretation of UAV data in agriculture signiﬁcantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workﬂow is able to automatize plant cataloging and training image extraction, especially for large datasets.

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