2024-09-30 イリノイ大学アーバナ・シャンペーン校
＜関連情報＞
- https://aces.illinois.edu/news/new-imaging-technique-brings-us-closer-simplified-low-cost-agricultural-quality-assessment
- https://www.sciencedirect.com/science/article/pii/S0260877424002899
- https://www.sciencedirect.com/science/article/pii/S2590123024008788
- https://www.sciencedirect.com/science/article/pii/S2772375524001382
ディープラーニングに基づくハイパースペクトル画像再構成による農産物の品質評価 Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product
Md. Toukir Ahmed, Ocean Monjur, Mohammed Kamruzzaman
Journal of Food Engineering Available online: 8 July 2024
DOI:https://doi.org/10.1016/j.jfoodeng.2024.112223
Highlights
- HSCNN-D reconstruction algorithm was applied to determine the SSC of sweet potato.
- VNIR hyperspectral camera was used to acquire images.
- Important wavebands were selected using GA.
- Hyperspectral images were reconstructed from their corresponding RGB images.
- The PLSR model applied on the reconstructed spectra showed impressive performance.
Abstract
Hyperspectral imaging (HSI) has recently emerged as a promising tool for many agricultural applications; however, the technology cannot be directly used in real-time for immediate decision-making and actions due to the extensive time needed to capture, process, and analyze large volumes of data. Consequently, the development of a simple, compact, and cost-effective imaging system is not possible with the current HSI systems. Therefore, the overall goal of this study was to reconstruct hyperspectral images from RGB images through deep learning for agricultural applications. Specifically, this study used Hyperspectral Convolutional Neural Network – Dense (HSCNN-D) to reconstruct hyperspectral images from RGB images for predicting soluble solid content (SSC) in sweet potatoes. The algorithm reconstructed the hyperspectral images from RGB images, with the resulting spectra closely matching the ground-truth. The partial least squares regression (PLSR) model based on reconstructed spectra outperformed the model using the full spectral range, demonstrating its potential for SSC prediction in sweet potatoes. These findings highlight the potential of deep learning-based hyperspectral image reconstruction as a low-cost, efficient tool for various agricultural uses.
ディープラーニングを用いたハイパースペクトル画像再構成の農業および生物学的応用における比較分析 Comparative analysis of hyperspectral Image reconstruction using deep learning for agricultural and biological applications
Md Toukir Ahmed, Arthur Villordon, Mohammed Kamruzzaman
Results in Engineering Available online: 27 July 2024
DOI:https://doi.org/10.1016/j.rineng.2024.102623
Highlights
- Compared HSCNN-D, HRNET, and MST++ algorithms for HS image reconstruction of sweet potato.
- Used VNIR hyperspectral camera (400–1000 nm) for image capture.
- Selected key wavebands with GA and assessed their importance via SHAP.
- Reconstructed images and spectra closely matched ground truth.
- Created prediction maps to show DMC distribution in sweet potatoes.
Abstract
Hyperspectral imaging (HSI) has become a key technology for non-invasive quality evaluation in various fields, offering detailed insights through spatial and spectral data. Despite its efficacy, the complexity and high cost of HSI systems have hindered their widespread adoption. This study addressed these challenges by exploring deep learning-based hyperspectral image reconstruction from RGB (Red, Green, Blue) images, particularly for agricultural products. Specifically, different hyperspectral reconstruction algorithms, such as Hyperspectral Convolutional Neural Network – Dense (HSCNN-D), High-Resolution Network (HRNET), and Multi-Scale Transformer Plus Plus (MST++), were compared to assess the dry matter content of sweet potatoes. Among the tested reconstruction methods, HRNET demonstrated superior performance, achieving the lowest mean relative absolute error (MRAE) of 0.07, root mean square error (RMSE) of 0.03, and the highest peak signal-to-noise ratio (PSNR) of 32.28 decibels (dB). Some key features were selected using the genetic algorithm (GA), and their importance was interpreted using explainable artificial intelligence (XAI). Partial least squares regression (PLSR) models were developed using the RGB, reconstructed, and ground truth (GT) data. The visual and spectra quality of these reconstructed methods was compared with GT data, and predicted maps were generated. The results revealed the prospect of deep learning-based hyperspectral image reconstruction as a cost-effective and efficient quality assessment tool for agricultural and biological applications.
鶏卵および孵化場産業の発展に向けた、ヒヨコ胚の死亡率予測のためのハイパースペクトル画像再構成 Hyperspectral image reconstruction for predicting chick embryo mortality towards advancing egg and hatchery industry
Md. Toukir Ahmed, Md Wadud Ahmed Ocean Monjur, Jason Lee Emmert, Girish Chowdhary, Mohammed Kamruzzaman
Smart Agricultural Technology Available online: 8 August 2024
DOI:https://doi.org/10.1016/j.atech.2024.100533
Abstract
As the demand for food surges and the agricultural sector undergoes a transformative shift towards sustainability and efficiency, the need for precise and proactive measures to ensure the health and welfare of livestock becomes paramount. In the egg and hatchery industry, hyperspectral imaging (HSI) has emerged as a cutting-edge, non-destructive technique for fast and accurate egg quality analysis, including detecting chick embryo mortality. However, the high cost and operational complexity compared to conventional RGB imaging are significant bottlenecks in the widespread adoption of HSI technology. To overcome these hurdles and unlock the full potential of HSI, a promising solution is hyperspectral image reconstruction from standard RGB images. This study aims to reconstruct hyperspectral images from RGB images for non-destructive early prediction of chick embryo mortality. Initially, the performance of different image reconstruction algorithms, such as HRNET, MST++, Restormer, and EDSR were compared to reconstruct the hyperspectral images of the eggs in the early incubation period. Later, the reconstructed spectra were used to differentiate live from dead embryos eggs using the XGBoost and Random Forest classification methods. Among the reconstruction methods, HRNET showed impressive reconstruction performance with MRAE of 0.0955, RMSE of 0.0159, and PSNR of 36.79 dB. This study motivated the idea that harnessing imaging technology integrated with smart sensors and data analytics has the potential to improve automation, enhance biosecurity, and optimize resource management towards sustainable agriculture 4.0.
