This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm designed to enable an ultra-compact ground robot to count corn stands by driving through the fields autonomously. The algorithm leverages a deep network to detect corn plants in images, and a visual tracking model to re-identify detected objects at different time steps. We collected data from 53 corn plots in various fields for corn plants around 14 days after emergence (stage V3 - V4). The robot predictions have agreed well with the ground truth with \(C_{robot}=1.02 \times C_{human}-0.86\) and a correlation coefficient \(R=0.96\). The mean relative error given by the algorithm is \(-3.78\%\), and the standard deviation is \(6.76\%\). These results indicate a first and significant step towards autonomous robot-based real-time phenotyping using low-cost, ultra-compact ground robots for corn and potentially other crops.

中文翻译：

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基于高精度控制和深度学习的农用机器人玉米架计数算法

本文提出了一种用于农业操作的低成本，超紧凑型3D打印和自主现场机器人的高精度控制和基于深度学习的玉米架计数算法。当前，植物性状，例如出苗率，生物量，活力和林分计数，是手动测量的。这是高度劳动密集型的并且容易出错。这种称为TerraSentia的机器人旨在自动进行植物性状的测量，以进行有效的表型分析，以替代手动测量。在本文中，我们制定了一种非线性移动视野估计器，该算法使用机载机器人传感器识别关键地形参数，并且基于学习的非线性模型预测控制可确保在未知的车轮-地形交互作用下进行高精度的路径跟踪。此外，我们开发了一种机器视觉算法，旨在使超紧凑型地面机器人能够自动驾驶田地来计算玉米林分。该算法利用深层网络检测图像中的玉米植株，并利用视觉跟踪模型在不同时间步长重新识别检测到的物体。我们在出苗后约14天（阶段V3-V4）从各个田地的53个玉米田收集了数据。机器人的预测与 我们在出苗后14天左右（阶段V3-V4）从各个领域的53个玉米田收集了数据。机器人的预测与 我们在出苗后约14天（阶段V3-V4）从各个田地的53个玉米田收集了数据。机器人的预测与\（C_ {robot} = 1.02 \ times C_ {human} -0.86 \）和一个相关系数\（R = 0.96 \）。该算法给出的平均相对误差为\（-3.78 \％\），标准偏差为\（6.76 \％\）。这些结果表明，使用低成本，超紧凑型地面机器人处理玉米和其他潜在作物的自主机器人实时表型研究迈出了重要的第一步。