Soybean production is susceptible to biotic and abiotic stresses, exacerbated
by extreme weather events. Water limiting stress, i.e. drought, emerges as a
significant risk for soybean production, underscoring the need for advancements
in stress monitoring for crop breeding and production. This project combines
multi-modal information to identify the most effective and efficient automated
methods to investigate drought response. We investigated a set of diverse
soybean accessions using multiple sensors in a time series high-throughput
phenotyping manner to: (1) develop a pipeline for rapid classification of
soybean drought stress symptoms, and (2) investigate methods for early
detection of drought stress. We utilized high-throughput time-series
phenotyping using UAVs and sensors in conjunction with machine learning (ML)
analytics, which offered a swift and efficient means of phenotyping. The
red-edge and green bands were most effective to classify canopy wilting stress.
The Red-Edge Chlorophyll Vegetation Index (RECI) successfully differentiated
susceptible and tolerant soybean accessions prior to visual symptom
development. We report pre-visual detection of soybean wilting using a
combination of different vegetation indices. These results can contribute to
early stress detection methodologies and rapid classification of drought
responses in screening nurseries for breeding and production applications.

大豆生产对生物和非生物胁迫非常敏感，极端天气事件加剧了这种胁迫。水限制胁迫，即干旱，成为大豆生产的一项重大风险，凸显了育种和生产中对压力监测的需求。本项目通过多模态信息的结合，以识别最有效和高效的自动化方法，探讨干旱响应的研究。我们以高通量时间序列表型分型的方式，通过使用无人机和传感器进行高通量时间序列表型分型，并结合机器学习分析，开发了快速分类大豆干旱胁迫症状的流程，并研究了早期干旱胁迫检测方法。我们发现红边和绿色波段对于分类植被枯萎胁迫是最有效的。在目视症状发展之前，红边叶绿素植被指数（RECI）成功区分了易感和耐旱的大豆资源。我们采用不同的植被指数的组合实现了早期检测大豆枯萎。这些结果可以为早期胁迫检测方法和育种和生产应用中的筛选苗圃快速分类干旱响应做出贡献。

大豆水分限制胁迫的多传感器和多时相高通量表型学监测和早期检测

Multi-Sensor and Multi-temporal High-Throughput Phenotyping for  Monitoring and Early Detection of Water-Limiting Stress in Soybean

Radiometric accuracy of data is crucial in quantitative precision
agriculture, to produce reliable and repeatable data for modeling and decision
making. The effect of exposure time and gain settings on the radiometric
accuracy of multispectral images was not explored enough. The goal of this
study was to determine if having a fixed exposure (FE) time during image
acquisition improved radiometric accuracy of images, compared to the default
auto-exposure (AE) settings. This involved quantifying the errors from
auto-exposure and determining ideal exposure values within which radiometric
mean absolute percentage error (MAPE) were minimal (< 5%). The results showed
that FE orthomosaic was closer to ground-truth (higher R2 and lower MAPE) than
AE orthomosaic. An ideal exposure range was determined for capturing canopy and
soil objects, without loss of information from under-exposure or saturation
from over-exposure. A simulation of errors from AE showed that MAPE < 5% for
the blue, green, red, and NIR bands and < 7% for the red edge band for exposure
settings within the determined ideal ranges and increased exponentially beyond
the ideal exposure upper limit. Further, prediction of total plant nitrogen
uptake (g/plant) using vegetation indices (VIs) from two different growing
seasons were closer to the ground truth (mostly, R2 > 0.40, and MAPE = 12 to
14%, p < 0.05) when FE was used, compared to the prediction from AE images
(mostly, R2 < 0.13, MAPE = 15 to 18%, p >= 0.05).

比较固定曝光时间和自动曝光设置对多光谱图像辐射测量精度的影响，发现固定曝光时间更接近地面真值，而通过估算得出了曝光范围，使得辐射平均绝对百分比误差最小 (<5%)，并使用固定曝光时间的图像进行植被指数预测，与真实值较为接近。

适用于表型鉴定和精准农业的多光谱相机曝光设置和辐射校准方法的选择

Selection of appropriate multispectral camera exposure settings and  radiometric calibration methods for applications in phenotyping and precision  agriculture

How can we effectively leverage the domain knowledge from remote sensing to
better segment agriculture land cover from satellite images? In this paper, we
propose a novel, model-agnostic, data-fusion approach for vegetation-related
computer vision tasks. Motivated by the various Vegetation Indices (VIs), which
are introduced by domain experts, we systematically reviewed the VIs that are
widely used in remote sensing and their feasibility to be incorporated in deep
neural networks. To fully leverage the Near-Infrared channel, the traditional
Red-Green-Blue channels, and Vegetation Index or its variants, we propose a
Generalized Vegetation Index (GVI), a lightweight module that can be easily
plugged into many neural network architectures to serve as an additional
information input. To smoothly train models with our GVI, we developed an
Additive Group Normalization (AGN) module that does not require extra
parameters of the prescribed neural networks. Our approach has improved the
IoUs of vegetation-related classes by 0.9-1.3 percent and consistently improves
the overall mIoU by 2 percent on our baseline.

本文提出了一种新颖的数据融合方法，旨在从卫星图像中更好地区分农业土地覆盖，并介绍了如何将遥感领域的领域知识与深度神经网络相结合以提高模型性能。