Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Albert, Paul; Saadeldin, Mohamed; Narayanan, Badri; Namee, Brian Mac; Hennessy, D.; O’Connor, Noel E.; McGuinness, Kevin

doi:10.1109/cvprw56347.2022.00170

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…This approach yielded a peak performance with az HRMSE ( RMSE in this paper) of 92.69. Subsequently, in the subsequent project, Albert [ 59 ] proposed a semi-supervised learning methodology in conjunction with high-resolution enhancement of drone imagery to estimate biomass, culminating in an improved HRMSE of 85.7. Comparative analysis of these two endeavors underscores the fundamental contributions of the present study.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model

Zhang,

Peng,

Chen

et al. 2024

Sensors

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Deep learning methodologies employed for biomass prediction often neglect the intricate relationships between labels and samples, resulting in suboptimal predictive performance. This paper introduces an advanced supervised contrastive learning technique, termed Improved Supervised Contrastive Deep Regression (SCDR), which is adept at effectively capturing the nuanced relationships between samples and labels in the feature space, thereby mitigating this limitation. Simultaneously, we propose the U-like Hierarchical Residual Fusion Network (BioUMixer), a bespoke biomass prediction network tailored for image data. BioUMixer enhances feature extraction from biomass image data, facilitating information exchange and fusion while considering both global and local features within the images. The efficacy of the proposed method is validated on the Pepper_Biomass dataset, which encompasses over 600 original images paired with corresponding biomass labels. The results demonstrate a noteworthy enhancement in deep regression tasks, as evidenced by performance metrics on the Pepper_Biomass dataset, including RMSE = 252.18, MAE = 201.98, and MAPE = 0.107. Additionally, assessment on the publicly accessible GrassClover dataset yields metrics of RMSE = 47.92, MAE = 31.74, and MAPE = 0.192. This study not only introduces a novel approach but also provides compelling empirical evidence supporting the digitization and precision improvement of agricultural technology. The research outcomes align closely with the identified problem and research statement, underscoring the significance of the proposed methodologies in advancing the field of biomass prediction through state-of-the-art deep learning techniques.

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Section: Discussionmentioning

confidence: 99%

Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model

Zhang,

Peng,

Chen

et al. 2024

Sensors

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“…Cap Q H et al [ 19 ] proposed an effective super-resolution method called LASSR for plant disease diagnosis. Albert P et al [ 20 ] transferred knowledge learned on ground-level images to raw drone images and estimated dry herbage biomass by applying super-resolution technology to raw drone images. These successes demonstrate the significant application value of super resolution in plant image-related tasks.…”

Section: Introductionmentioning

confidence: 99%

PlantSR: Super-Resolution Improves Object Detection in Plant Images

Jiang,

Yu,

Zhong

et al. 2024

J. Imaging

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Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improving model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-of-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, with the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, with the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets related to this study are available at Github.

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“…Cap Q H et al [19] proposed an effective super-resolution method called LASSR, for Plant disease diagnosis. Albert P et al [20] transferred knowledge learned on ground-level images to raw drone images and estimated dry herbage biomass by applying superresolution technology to raw drone images. These successes demonstrate the significant application value of super-resolution in plant image-related tasks.…”

Section: Introductionmentioning

confidence: 99%

PlantSR: Super-Resolution Improves Object Detection in Plant Images

Jiang,

Zhong,

Shao

et al. 2024

Preprint

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Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improve model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-or-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, on the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, on the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets are available at https://github.com/SkyCol/PlantSR.

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Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Cited by 7 publications

References 41 publications

Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model

Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model

PlantSR: Super-Resolution Improves Object Detection in Plant Images

PlantSR: Super-Resolution Improves Object Detection in Plant Images

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