Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.).

IF 7.6 1区 农林科学 Q1 AGRONOMY
Plant Phenomics Pub Date : 2024-11-28 eCollection Date: 2024-01-01 DOI:10.34133/plantphenomics.0278
Leonardo Volpato, Evan M Wright, Francisco E Gomez
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引用次数: 0

Abstract

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.

基于无人机的干豆(Phaseolus vulgaris L.)相对成熟度、林分数和株高的数字表型评价
在试验田,人工跟踪植物成熟期、测定早期植株密度和作物高度已经做了大量的工作。本研究探索了RGB无人机图像和深度学习(DL)方法来测量相对成熟度(RM)、林分数(SC)和植物高度(PH),可能提供比传统方法更高的吞吐量、准确性和成本效益。利用无人机图像的时间序列,采用卷积神经网络(CNN)和长短期记忆(LSTM)混合模型估计干豆RM。对于早期SC评估,评估了更快的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来增强DL模型的性能。PH值采用分位数法从数字曲面模型(DSM)和点云(PC)数据源中获得。CNN-LSTM模型在各种条件下的RM预测精度较高,优于传统的图像预处理方法。加入生长日数(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物,比传统方法具有更高的准确性和不同飞行高度的一致性。对于PH估计,在分析的两个数据集中观察到与真实数据的适度相关性。PC和DSM源数据的选择可能取决于具体的环境和飞行条件。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。在没有精确地面高程数据的情况下,用于估算PH的减法方法的结果与基于差分的方法相当。此外,开发的管道和开源软件具有显著造福表型社区的潜力。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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