Temporal prediction and evaluation of Brassica growth in the field using conditional generative adversarial networks
نویسندگان
چکیده
• Neural network-based growth models are able to forecast plant growth. GANs produce realistic, reliable, and reasonable images of future stages. Generated all phases allow the derivation phenotypic traits. Increased explainability compared direct prediction individual parameters. Farmers frequently assess performance as basis for making decisions when take action in field, such fertilization, weed control, or harvesting. The is a major challenge, it affected by numerous highly variable environmental factors. This paper proposes novel monitoring approach that comprises high-throughput imaging sensor measurements their automatic analysis predict Our approach’s core machine learning-based generative model based on conditional adversarial networks, which appearance plants. In experiments with RGB time series laboratory-grown Arabidopsis thaliana field-grown cauliflower plants, we show our produces interpretation generated through neural instance segmentation allows various traits describe
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ژورنال
عنوان ژورنال: Computers and Electronics in Agriculture
سال: 2021
ISSN: ['1872-7107', '0168-1699']
DOI: https://doi.org/10.1016/j.compag.2021.106415