Farmers 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.
翻译:农民经常评估植物增长和绩效,以此作为决策何时在实地采取行动的基础,如肥料、杂草控制或收获。预测植物增长是一项重大挑战,因为它受到众多和高度可变的环境因素的影响。本文件提出一种新的监测方法,其中包括高通量成像传感器测量和自动分析,以预测未来的植物增长。我们的方法核心是建立在有条件的基因对抗网络基础上的基于学习的新机器的基因增殖模型,能够预测个别植物的未来外观。在对实验室培育的阿拉伯idopsis Thaliana 和野生花椰菜植物的RGB时间序列图像的实验中,我们表明我们的方法产生了现实、可靠和合理的未来增长阶段图像。通过神经网络的实例分割对产生的图像进行自动解释,可以产生描述植物增长的各种花纹。