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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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使用无人机图像和机器学习评估大豆住宿.

Shagor Sarkar¹, Jing Zhou², Andrew Scaboo¹

¹Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA.

Plants (Basel, Switzerland)

|August 26, 2023

概括

无人驾驶飞行器 (UAV) 图像与机器学习相结合,有效地评估大豆寄宿,这是一个关键的育种特征. 这种自动化方法比传统的视觉评估提高了准确性和效率.

关键词:

农作物育种植物育种高通量表型化 (High-Throughput Phenotyping) 是一种高通量的表型化.图像的特征图像的特征图像的特征远程传感是一种遥感技术.

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科学领域:

农业科学农业科学
植物育种植物育种
遥感遥感遥感遥感

背景情况:

植物寄宿是一种关键的大豆表型,用于繁殖,传统上以视觉限制来评估.
视觉评估是耗时的,容易出现人为错误,需要先进的方法.

研究的目的:

探索基于无人机 (UAV) 的成像和机器学习对大豆存放评估的潜力.
开发一种自动化系统,用于在育种计划中对大豆种植严重程度进行分类.

主要方法:

在繁殖阶段收集了1266个大豆地块的无人机RGB图像.
分段地图和提取12个图像特征用于住宿评估.
评估了四种机器学习模型 (XGBoost,RF,KNN,ANN) 使用SMOTE-ENN预处理不平衡数据.

主要成果:

合成少数群体过量采样-编辑最近邻居 (SMOTE-ENN) 预处理方法改善了所有模型的分类准确性.
人工神经网络 (ANN) 分类器在使用SMOTE-ENN处理的数据集时实现了96%的整体准确性.
该研究证明了无人机图像和机器学习在区分大豆寄宿表型方面的有效性.

结论:

基于无人机的图像和机器学习为大豆住宿评估提供了强大而准确的方法.
开发的分类模型可以整合到繁殖计划中,以有效评估表型.
在此分类任务中,SMOTE-ENN 是适合不平衡数据集的预处理技术.