基于多条件生成对抗网络的时变生成图像数据驱动作物生长模拟
Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks
AI总结：
本研究提出两阶段多条件生成对抗网络框架，可结合多种生长条件生成时变作物图像并用于表型分析，能衔接图像与过程类作物生长模型，助力精准农业。
摘要：
基于图像的作物生长建模可通过揭示作物随时间的空间发育情况，为精准农业提供重要支持，从而能够早期、针对特定位置预估未来相关植物性状，如叶面积或生物量。生成真实清晰的作物图像的前提是，在模型中整合多种影响生长的条件，例如初始生长阶段的图像、对应的生长时间，以及田间处理的更多信息。我们提出了一个两阶段框架，第一阶段为图像预测模型，第二阶段为生长估算模型，二者独立训练。图像预测模型是条件Wasserstein生成对抗网络（CWGAN，conditional Wasserstein generative adversarial network）。在该模型的生成器中，使用条件批量归一化（CBN，conditional batch normalization）将不同条件与输入图像整合。这使得模型能够根据不同类型的多种影响因素，生成随时间变化的人工图像。框架的第二部分利用这些图像进行植物表型分析，推导植物特异性性状，并将其与非人工（真实）参考图像的性状进行比较。在多种作物数据集上，该框架可生成真实、清晰的图像预测结果，从短期预测到长期预测，图像质量会略有下降。使用训练好的框架对不同生长影响条件进行模拟，可为这些因素与作物外观的关联提供有价值的见解，这在复杂、研究较少的作物混作系统中尤为有用。进一步结果表明，添加基于过程的模拟生物量作为条件，可提高从预测图像中推导的表型性状的准确性。这证明了我们的框架有潜力作为基于图像和基于过程的作物生长模型之间的接口。
英文摘要：
Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. We present a two-stage framework consisting first of an image prediction model and second of a growth estimation model, which both are independently trained. The image prediction model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate different conditions along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors of different kinds. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. For various crop datasets, the framework allows realistic, sharp image predictions with a slight loss of quality from short-term to long-term predictions. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between an image- and process-based crop growth model.
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