Buxbaum Nicolas, Lieth Johann Heinrich, Earles Mason
Department of Biological and Agricultural Engineering, University of California, Davis, Davis, CA, United States.
Department of Plant Sciences, University of California, Davis, Davis, CA, United States.
Front Plant Sci. 2022 Apr 13;13:758818. doi: 10.3389/fpls.2022.758818. eCollection 2022.
Plant breeders, scientists, and commercial producers commonly use growth rate as an integrated signal of crop productivity and stress. Plant growth monitoring is often done destructively growth rate estimation by harvesting plants at different growth stages and simply weighing each individual plant. Within plant breeding and research applications, and more recently in commercial applications, non-destructive growth monitoring is done using computer vision to segment plants in images from the background, either in 2D or 3D, and relating these image-based features to destructive biomass measurements. Recent advancements in machine learning have improved image-based localization and detection of plants, but such techniques are not well suited to make biomass predictions when there is significant self-occlusion or occlusion from neighboring plants, such as those encountered under leafy green production in controlled environment agriculture. To enable prediction of plant biomass under occluded growing conditions, we develop an end-to-end deep learning approach that directly predicts lettuce plant biomass from color and depth image data as provided by a low cost and commercially available sensor. We test the performance of the proposed deep neural network for lettuce production, observing a mean prediction error of 7.3% on a comprehensive test dataset of 864 individuals and substantially outperforming previous work on plant biomass estimation. The modeling approach is robust to the busy and occluded scenes often found in commercial leafy green production and requires only measured mass values for training. We then demonstrate that this level of prediction accuracy allows for rapid, non-destructive detection of changes in biomass accumulation due to experimentally induced stress induction in as little as 2 days. Using this method growers may observe and react to changes in plant-environment interactions in near real time. Moreover, we expect that such a sensitive technique for non-destructive biomass estimation will enable novel research and breeding of improved productivity and yield in response to stress.
植物育种者、科学家和商业生产者通常将生长速率作为作物生产力和胁迫的综合信号。植物生长监测通常采用破坏性方法，即在不同生长阶段收获植物并简单地对每株植物称重来估计生长速率。在植物育种和研究应用中，以及最近在商业应用中，非破坏性生长监测是通过计算机视觉来完成的，即从背景中分割二维或三维图像中的植物，并将这些基于图像的特征与破坏性生物量测量值相关联。机器学习的最新进展改进了基于图像的植物定位和检测，但当存在严重的自我遮挡或来自相邻植物的遮挡时，如在可控环境农业中的绿叶蔬菜生产中遇到的情况，这些技术不太适合进行生物量预测。为了能够预测在遮挡生长条件下的植物生物量，我们开发了一种端到端的深度学习方法，该方法直接根据低成本且商用的传感器提供的颜色和深度图像数据预测生菜植物生物量。我们测试了所提出的深度神经网络在生菜生产中的性能，在一个由864个个体组成的综合测试数据集上观察到平均预测误差为7.3%，并且显著优于先前关于植物生物量估计的工作。该建模方法对于商业绿叶蔬菜生产中常见的繁忙和遮挡场景具有鲁棒性，并且只需要测量的质量值进行训练。然后，我们证明了这种预测精度水平能够在短短2天内快速、非破坏性地检测由于实验诱导的胁迫导致的生物量积累变化。使用这种方法，种植者可以近乎实时地观察植物 - 环境相互作用的变化并做出反应。此外，我们期望这种用于非破坏性生物量估计的灵敏技术将能够开展关于提高生产力和应对胁迫的产量的新研究和育种工作。
