A detailed study on accuracy of uncooled thermal cameras by exploring the data collection workflow

Zhao, Tiebiao; Niu, Haoyu; Anderson, Andreas; Chen, YangQuan; Viers, Joshua H.

doi:10.1117/12.2305217

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Common processing algorithms include non-uniform correction, digital enhancement, and denoising. These algorithms can improve the quality of infrared images and improve detection accuracy [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

“…Cooled thermal imaging cameras typically have smaller pixel pitches [ 22 ]. However, their high costs, large dimensions, and short working times also increase the difficulty and uncertainty of their use [ 23 ]. With ongoing advances in detector manufacturing, the thermal imager has gradually overcome the constraints of low-temperature cooling during operation and large volume and has also compensated for the low sensitivity in ambient temperature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gas Imaging with Uncooled Thermal Imager

Zhang,

Chen,

Lin

et al. 2024

Sensors

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Gas imaging has become one of the research hotspots in the field of gas detection due to its significant advantages, such as high efficiency, large range, and dynamic visualization. It is widely used in industries such as natural gas transportation, chemical, and electric power industries. With the development of infrared detector technology, uncooled thermal imagers are undergoing a developmental stage of technological advancement and widespread application. This article introduces a gas imaging principle and radiation transfer model, focusing on passive imaging technology and active imaging technology. Combined with the actual analysis, the application scenarios using uncooled thermal imaging cameras for gas imaging measurement are analyzed. Finally, the limitations and challenges of the development of gas imaging technology are analyzed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gas Imaging with Uncooled Thermal Imager

Zhang,

Chen,

Lin

et al. 2024

Sensors

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“…In recent times, Unmanned Aerial Vehicles (UAVs) have emerged as valuable assets in diverse agricultural contexts, including the forecasting of yield [ 8 , 9 ], management of irrigation [ 10 , 11 ], and estimation of water stress [ 12 , 13 ]. Through the incorporation of lightweight sensors onto UAV platforms, it has become viable to capture imagery with exceptional spatial and temporal resolution at a minimal expense [ 14 , 15 ]. With ML regression models, such as support vector regression (SVR) [ 16 ] and random forest regression (RFR) methods [ 17 ], empirical regression models have been developed between crop yield and crop canopy features, such as vegetation indices [ 8 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery

Niu,

Peddagudreddygari,

Bhandari

et al. 2024

Sensors

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In the pursuit of sustainable agriculture, efficient water management remains crucial, with growers relying on advanced techniques for informed decision-making. Cotton yield prediction, a critical aspect of agricultural planning, benefits from cutting-edge technologies. However, traditional methods often struggle to capture the nuanced complexities of crop health and growth. This study introduces a novel approach to cotton yield prediction, leveraging the synergy between Unmanned Aerial Vehicles (UAVs) and scale-aware convolutional neural networks (CNNs). The proposed model seeks to harness the spatiotemporal dynamics inherent in high-resolution UAV imagery to improve the accuracy of the cotton yield prediction. The CNN component adeptly extracts spatial features from UAV-derived imagery, capturing intricate details related to crop health and growth, modeling temporal dependencies, and facilitating the recognition of trends and patterns over time. Research experiments were carried out in a cotton field at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, with three replications evaluating four irrigation treatments (rainfed, full irrigation, percent deficit of full irrigation, and time delay of full irrigation) on cotton yield. The prediction revealed that the proposed CNN regression models outperformed conventional CNN models, such as AlexNet, CNN-3D, CNN-LSTM, ResNet. The proposed CNN model showed state-of-art performance at different image scales, with the R2 exceeding 0.9. At the cotton row level, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.08 pounds per row and 7.76%, respectively. At the cotton grid level, the MAE and MAPE were 0.05 pounds and 10%, respectively. This shows the proposed model’s adaptability to the dynamic interplay between spatial and temporal factors that affect cotton yield. The authors conclude that integrating UAV-derived imagery and CNN regression models is a potent strategy for advancing precision agriculture, providing growers with a powerful tool to optimize cultivation practices and enhance overall cotton productivity.

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“…In recent years, UAVs have emerged as powerful tools for various agricultural applications, including irrigation management [12,13] and water stress estimation [14,15]. With the integration of lightweight sensors on UAV platforms, it has become feasible to capture high-resolution imagery with excellent spatial and temporal resolution at a low cost [16,17]. RGB, thermal, and multispectral cameras are commonly utilized in agricultural research due to their lightweight nature and low power consumption [18,19].…”

Section: Introductionmentioning

confidence: 99%

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

Niu,

Landivar,

Duffield

2024

Advan. Mod. Agr.

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<p>Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.<strong></strong></p>

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A detailed study on accuracy of uncooled thermal cameras by exploring the data collection workflow

Cited by 5 publications

References 12 publications

Gas Imaging with Uncooled Thermal Imager

Gas Imaging with Uncooled Thermal Imager

In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

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