Volpato Leonardo, Wright Evan M, Gomez Francisco E
Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA.
Plant Phenomics. 2024 Nov 28;6:0278. doi: 10.34133/plantphenomics.0278. eCollection 2024.
Substantial effort has been made in manually tracking plant maturity and to measure early-stage plant density and crop height in experimental fields. In this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH), potentially offering higher throughput, accuracy, and cost-effectiveness than traditional methods. A time series of drone images was utilized to estimate dry bean RM employing a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model. For early-stage SC assessment, Faster RCNN object detection algorithm was evaluated. Flight frequencies, image resolution, and data augmentation techniques were investigated to enhance DL model performance. PH was obtained using a quantile method from digital surface model (DSM) and point cloud (PC) data sources. The CNN-LSTM model showed high accuracy in RM prediction across various conditions, outperforming traditional image preprocessing approaches. The inclusion of growing degree days (GDD) data improved the model's performance under specific environmental stresses. The Faster R-CNN model effectively identified early-stage bean plants, demonstrating superior accuracy over traditional methods and consistency across different flight altitudes. For PH estimation, moderate correlations with ground-truth data were observed across both datasets analyzed. The choice between PC and DSM source data may depend on specific environmental and flight conditions. Overall, the CNN-LSTM and Faster R-CNN models proved more effective than conventional techniques in quantifying RM and SC. The subtraction method proposed for estimating PH without accurate ground elevation data yielded results comparable to the difference-based method. Additionally, the pipeline and open-source software developed hold potential to significantly benefit the phenotyping community.
在实验田中，人们在人工跟踪作物成熟度以及测量早期作物密度和株高方面付出了巨大努力。在本研究中，探索了利用RGB无人机图像和深度学习（DL）方法来测量相对成熟度（RM）、植株计数（SC）和株高（PH），与传统方法相比，这些方法可能具有更高的通量、准确性和成本效益。利用无人机图像的时间序列，采用混合卷积神经网络（CNN）和长短期记忆（LSTM）模型来估计干豆的RM。对于早期SC评估，评估了Faster RCNN目标检测算法。研究了飞行频率、图像分辨率和数据增强技术以提高DL模型性能。使用分位数方法从数字表面模型（DSM）和点云（PC）数据源获取PH。CNN-LSTM模型在各种条件下的RM预测中显示出高精度，优于传统图像预处理方法。纳入生长度日（GDD）数据提高了模型在特定环境胁迫下的性能。Faster R-CNN模型有效地识别了早期豆类植株，与传统方法相比显示出更高的准确性，并且在不同飞行高度上具有一致性。对于PH估计，在分析的两个数据集中均观察到与地面真值数据存在中等相关性。PC和DSM源数据之间的选择可能取决于特定的环境和飞行条件。总体而言，CNN-LSTM和Faster R-CNN模型在量化RM和SC方面比传统技术更有效。在没有准确地面高程数据的情况下提出的用于估计PH的减法方法产生的结果与基于差值的方法相当。此外，开发的流程和开源软件有潜力使表型分析社区显著受益。
