Wheat Spikes Height Estimation Using Stereo Cameras

Amirhossein Zaji;Zheng Liu;Gaozhi Xiao;Pankaj Bhowmik;Jatinder S. Sangha;Yuefeng Ruan
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引用次数: 1

Abstract

There is a positive correlation between wheat plant height and lodging, yield, and biomass. So, in precision agriculture, a high-throughput estimation of the wheat plant's height in terms of its spikes is essential. This study aims to develop a straightforward, cost-effective method for measuring the height of wheat plants using stereo cameras. To collect the required datasets, we conducted an experiment in which we collected RGB images along with their depth layer using two renowned stereo cameras, OAKD and D455. Then, we used a deep learning model called mask region-based convolutional neural networks to localize and distinguish the spikes in the collected images. In this study, we localized the wheat spikes using object detection (OD) and instance segmentation (IS) models. The advantage of the OD model over the IS model is that its bounding box annotation procedure in the data preparation phase is significantly faster than the IS model's polygon annotation. However, the disadvantage of OD is that there are many background pixels in each predicted bounding box, which reduces the performance of height estimation. To facilitate the annotation process of the collected datasets, we also developed a hybrid scale-invariant feature transform random forest-based active learning algorithm to transfer the annotations of one camera to the other. The results show that the OAKD camera performs better than the D455 camera for wheat plant height estimation due to its higher RGB quality and better matching of the mono camera images. Using the OAKD camera and IS model, the algorithm proposed in this study is able to estimate wheat height with mean absolute percentage error values of 0.75% and 0.67% at the spike and plot levels, respectively.
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利用立体相机估算小麦穗高
小麦株高与倒伏、产量和生物量呈正相关。因此,在精准农业中,通过高通量估计小麦植株的穗高是至关重要的。这项研究旨在开发一种简单、经济高效的方法,使用立体相机测量小麦植株的高度。为了收集所需的数据集,我们进行了一项实验,使用两个著名的立体相机OAKD和D455收集RGB图像及其深度层。然后,我们使用一种称为基于掩模区域的卷积神经网络的深度学习模型来定位和区分采集图像中的尖峰。在本研究中,我们使用对象检测(OD)和实例分割(IS)模型对小麦穗进行了定位。OD模型相对于IS模型的优势在于,其在数据准备阶段的边界框注释过程明显快于IS模型的多边形注释。然而,OD的缺点是在每个预测的边界框中都有许多背景像素,这降低了高度估计的性能。为了方便收集数据集的注释过程,我们还开发了一种基于比例不变特征变换随机森林的混合主动学习算法,将一个相机的注释转移到另一个相机。结果表明,OAKD相机在小麦株高估计方面比D455相机表现更好,因为它具有更高的RGB质量和更好的单相机图像匹配性。利用OAKD相机和IS模型,本研究提出的算法能够在穗部和小区水平上估计小麦高度,平均绝对百分误差值分别为0.75%和0.67%。
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2024 Index IEEE Transactions on AgriFood Electronics Vol. 2 Table of Contents Front Cover IEEE Circuits and Systems Society Information IEEE Circuits and Systems Society Information
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