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通过近距离传感来估计小麦器官的生物物理变量,比较CNN和PLSr

Alexis Carlier¹, Sébastien Dandrifosse¹, Benjamin Dumont²

¹Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium.

Frontiers in plant science

|December 6, 2023

概括

深度学习模型,特别是卷积神经网络 (CNN),通过使用伪标签等高级培训技术,有效估计作物生物物理变量. 这种方法克服了作物表型化中的数据稀缺性,以改善产量预测.

科学领域:

农业科学农业科学
遥感遥感遥感遥感
计算机科学 (深度学习)

背景情况:

准确估计生物物理植被变量对于作物监测和产量预测至关重要.
传统的遥感方法由于复杂的工厂结构和需要广泛的特征工程而面临挑战.
有限的标记数据阻碍了深度学习的应用,特别是卷积神经网络 (CNN),用于回归任务的作物表型.

研究的目的:

评估各种CNN模型在预测小麦干物质,吸收和度方面的有效性.
通过使用一种新的培训管道,应对作物表型化中有限的标记数据的挑战.
将CNN的性能与生物物理变量估计的传统机器学习方法进行比较.

主要方法:

利用RGB和多光谱图像从小麦耕作到成熟.
开发了一个培训管道,包括转移学习,未标记数据的伪标签和时间关系校正.
对比了不同CNN架构 (EfficientNetB4,Resnet50) 和部分最小平方回归 (PLSr) 模型的性能.

主要成果:

在使用伪标签方法时,CNN模型显著提高了预测准确度.
EfficientNetB4实现了地表生物质预测的最高准确度 (R2 = 0.92).
在预测叶面积指数 (LAI),吸收和度 (R2 = 0.82,0.73和0.80,分别) 方面,Resnet50表现出色.

结论:

通过伪标签增强的CNN,为表型定量作物生物物理变量提供了一个有希望和可访问的解决方案.
开发的培训管道有效地克服了作物科学应用的深度学习中的数据稀缺问题.
需要进一步的研究才能充分实现CNN在先进的作物表型和管理中的潜力.

关键词:

在美国,CNN是CNN.PLSrr PLSr 在线阅读生物物理变量生物物理变量接近距离的传感器多任务处理能力.现型定制现型定制小麦小麦小麦小麦小麦小麦小麦.

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