Early identification of drought stress in crops is vital for implementing
effective mitigation measures and reducing yield loss. Non-invasive imaging
techniques hold immense potential by capturing subtle physiological changes in
plants under water deficit. Sensor based imaging data serves as a rich source
of information for machine learning and deep learning algorithms, facilitating
further analysis aimed at identifying drought stress. While these approaches
yield favorable results, real-time field applications requires algorithms
specifically designed for the complexities of natural agricultural conditions.
Our work proposes a novel deep learning framework for classifying drought
stress in potato crops captured by UAVs in natural settings. The novelty lies
in the synergistic combination of a pretrained network with carefully designed
custom layers. This architecture leverages feature extraction capabilities of
the pre-trained network while the custom layers enable targeted dimensionality
reduction and enhanced regularization, ultimately leading to improved
performance. A key innovation of our work involves the integration of
Gradient-Class Activation Mapping (Grad-CAM), an explainability technique.
Grad-CAM sheds light on the internal workings of the deep learning model,
typically referred to as a black box. By visualizing the focus areas of the
model within the images, Grad-CAM fosters interpretability and builds trust in
the decision-making process of the model. Our proposed framework achieves
superior performance, particularly with the DenseNet121 pre-trained network,
reaching a precision of 98% to identify the stressed class with an overall
accuracy of 90%. Comparative analysis of existing state-of-the-art object
detection algorithms reveals the superiority of our approach in significantly
higher precision and accuracy.

我们提出了一种深度学习框架，通过结合预训练网络和自定义层，实现了在自然环境下由无人机捕获的土豆作物的干旱胁迫分类。这一创新性工作的关键在于整合了 Grad-CAM 解释技术，通过可视化模型在图像中的焦点区域，提高了模型的解释性和决策过程的可信度。相较于现有的物体检测算法，我们的方法在精确度和准确度上表现出明显的优势。

可解释的轻量级深度学习流程提升干旱压力

Explainable Light-Weight Deep Learning Pipeline for Improved Drought  Stres

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

Quantification of physiological changes in plants can capture different
drought mechanisms and assist in selection of tolerant varieties in a high
throughput manner. In this context, an accurate 3D model of plant canopy
provides a reliable representation for drought stress characterization in
contrast to using 2D images. In this paper, we propose a novel end-to-end
pipeline including 3D reconstruction, segmentation and feature extraction,
leveraging deep neural networks at various stages, for drought stress study. To
overcome the high degree of self-similarities and self-occlusions in plant
canopy, prior knowledge of leaf shape based on features from deep siamese
network are used to construct an accurate 3D model using structure from motion
on wheat plants. The drought stress is characterized with a deep network based
feature aggregation. We compare the proposed methodology on several
descriptors, and show that the network outperforms conventional methods.

本文提出了一种新颖的端到端 pipeline，利用深度神经网络，在 3D 重构、分割和特征提取等不同阶段实现植物干旱胁迫的研究和表征，使用基于 deep siamese network 提取的特征知识构建了准确的植物冠层 3D 模型，并利用深度学习方法进行特征聚合，实现对植物干旱胁迫的表征和研究。结果表明，与传统方法相比，该方法具有更优异的性能。