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.

翻译：基于图像的作物生长建模通过揭示作物在时间维度上的空间发育过程，可显著推动精准农业发展，从而实现对叶面积、生物量等关键未来植株性状的早期及位置特异性评估。生成清晰逼真的作物图像需将多种生长影响因素（如初始生长阶段图像、对应生长时间及田间管理信息）整合至模型中。本文提出包含图像预测模型与生长估计模型的两阶段框架，两个模型均独立训练。图像预测模型采用条件Wasserstein生成对抗网络（CWGAN），其生成器中通过条件批归一化（CBN）将不同条件与输入图像融合，使模型能基于多种异质影响因素生成随时间变化的人工图像。框架第二阶段利用这些图像进行植物表型分析：提取植株特异性性状并与真实参考图像对比。在多种作物数据集上，该框架可实现清晰逼真的图像预测，从短期到长期预测仅出现轻微质量损失。训练框架对不同生长影响条件的模拟，为揭示各因素与作物表观关联性提供了重要见解，尤其在复杂且研究较少的作物混种系统中效果显著。进一步结果表明，将基于过程的模拟生物量作为附加条件时，预测图像导出的表型性状精度得到提升。这证明了本框架作为连接基于图像与基于过程作物生长模型桥梁的潜力。