Thermal cameras are an important tool for agricultural research because they
allow for non-invasive measurement of plant temperature, which relates to
important photochemical, hydraulic, and agronomic traits. Utilizing low-cost
thermal cameras can lower the barrier to introducing thermal imaging in
agricultural research and production. This paper presents an approach to
improve the temperature accuracy and image quality of low-cost thermal imaging
cameras for agricultural applications. Leveraging advancements in computer
vision techniques, particularly deep learning networks, we propose a method,
called $\textbf{VisTA-SR}$ ($\textbf{Vis}$ual \& $\textbf{T}$hermal
$\textbf{A}$lignment and $\textbf{S}$uper-$\textbf{R}$esolution Enhancement)
that combines RGB and thermal images to enhance the capabilities of
low-resolution thermal cameras. The research includes calibration and
validation of temperature measurements, acquisition of paired image datasets,
and the development of a deep learning network tailored for agricultural
thermal imaging. Our study addresses the challenges of image enhancement in the
agricultural domain and explores the potential of low-cost thermal cameras to
replace high-resolution industrial cameras. Experimental results demonstrate
the effectiveness of our approach in enhancing temperature accuracy and image
sharpness, paving the way for more accessible and efficient thermal imaging
solutions in agriculture.

通过结合 RGB 和热成像图像，采用计算机视觉技术中的深度学习网络，提出了一种名为 VisTA-SR（Visual & Thermal Alignment and Super-Resolution Enhancement）的方法，以增强低成本热成像相机的温度准确性和图像质量，进一步探索了低成本热成像相机在农业领域替代高分辨率工业相机的潜力，并证明了该方法在提高温度准确性和图像清晰度方面的有效性，为农业领域提供了更可靠和高效的热成像解决方案。

VisTA-SR: 提升农业低成本热像相机的准确度和分辨率

VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal  Imaging Cameras for Agriculture

In this work we introduce the CitrusFarm dataset, a comprehensive multimodal
sensory dataset collected by a wheeled mobile robot operating in agricultural
fields. The dataset offers stereo RGB images with depth information, as well as
monochrome, near-infrared and thermal images, presenting diverse spectral
responses crucial for agricultural research. Furthermore, it provides a range
of navigational sensor data encompassing wheel odometry, LiDAR, inertial
measurement unit (IMU), and GNSS with Real-Time Kinematic (RTK) as the
centimeter-level positioning ground truth. The dataset comprises seven
sequences collected in three fields of citrus trees, featuring various tree
species at different growth stages, distinctive planting patterns, as well as
varying daylight conditions. It spans a total operation time of 1.7 hours,
covers a distance of 7.5 km, and constitutes 1.3 TB of data. We anticipate that
this dataset can facilitate the development of autonomous robot systems
operating in agricultural tree environments, especially for localization,
mapping and crop monitoring tasks. Moreover, the rich sensing modalities
offered in this dataset can also support research in a range of robotics and
computer vision tasks, such as place recognition, scene understanding, object
detection and segmentation, and multimodal learning. The dataset, in
conjunction with related tools and resources, is made publicly available at
this https URL

该研究介绍了 CitrusFarm 数据集，这是由一个在农田中操作的轮式移动机器人收集的综合多模态感官数据集。该数据集提供了包含深度信息的立体 RGB 图像，以及单色、近红外和热红外图像，提供农业研究所需的多样光谱响应。此外，它还提供了包括轮式里程计、激光雷达、惯性测量单元（IMU）和具有实时动态差分（RTK）的 GNSS 的导航传感器数据，作为以厘米级精度的定位基准。该数据集包含在三个柑橘树区域收集的七个序列，涵盖了不同生长阶段、独特的种植模式和不同的日光条件的各种树种。它总共运行时间为 1.7 小时，覆盖了 7.5 公里的距离，共计 1.3 TB 的数据。我们预计这个数据集可以促进在农业树木环境中操作的自主机器人系统的发展，特别是用于定位、地图制作和作物监测任务。此外，该数据集提供的丰富感知模式还可以支持一系列机器人学和计算机视觉任务的研究，例如地点识别、场景理解、物体检测和分割以及多模态学习。该数据集与相关工具和资源一起，已在该 https URL 上公开提供。

柑橘园定位、建图与农作物监测的多模态数据集

Multimodal Dataset for Localization, Mapping and Crop Monitoring in  Citrus Tree Farms

Plant breeders and agricultural researchers can increase crop productivity by
identifying desirable features, disease resistance, and nutritional content by
analysing the Dry Bean dataset. This study analyses and compares different
Support Vector Machine (SVM) classification algorithms, namely linear,
polynomial, and radial basis function (RBF), along with other popular
classification algorithms. The analysis is performed on the Dry Bean Dataset,
with PCA (Principal Component Analysis) conducted as a preprocessing step for
dimensionality reduction. The primary evaluation metric used is accuracy, and
the RBF SVM kernel algorithm achieves the highest Accuracy of 93.34%, Precision
of 92.61%, Recall of 92.35% and F1 Score as 91.40%. Along with adept
visualization and empirical analysis, this study offers valuable guidance by
emphasizing the importance of considering different SVM algorithms for complex
and non-linear structured datasets.

通过对 Dry Bean 数据集的分析，支持向量机（SVM）的径向基函数（RBF）算法在准确率、精度、召回率和 F1 得分等方面均表现最佳，这为复杂和非线性结构数据集中考虑不同 SVM 算法的重要性提供了宝贵的指导。