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000904496 1001_ $$0P:(DE-HGF)0$$aDrees, Lukas$$b0$$eCorresponding author
000904496 245__ $$aTemporal prediction and evaluation of Brassica growth in the field using conditional generative adversarial networks
000904496 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2021
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000904496 520__ $$aFarmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach’s core is a novel machine learning-based generative growth model based on conditional generative adversarial networks, which is able to predict the future appearance of individual plants. In experiments with RGB time series images of laboratory-grown Arabidopsis thaliana and field-grown cauliflower plants, we show that our approach produces realistic, reliable, and reasonable images of future growth stages. The automatic interpretation of the generated images through neural network-based instance segmentation allows the derivation of various phenotypic traits that describe plant growth.
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000904496 7001_ $$0P:(DE-Juel1)168454$$aJunker-Frohn, Laura Verena$$b1$$ufzj
000904496 7001_ $$0P:(DE-HGF)0$$aKierdorf, Jana$$b2
000904496 7001_ $$0P:(DE-Juel1)186079$$aRoscher, Ribana$$b3$$ufzj
000904496 773__ $$0PERI:(DE-600)2016151-7$$a10.1016/j.compag.2021.106415$$gVol. 190, p. 106415 -$$p106415 -$$tComputers and electronics in agriculture$$v190$$x0168-1699$$y2021
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