Portable Hyperspectral Imaging System Research Articles

Non-Destructive Prediction of Rosmarinic Acid Content in Basil Plants Using a Portable Hyperspectral Imaging System and Ensemble Learning Algorithms

Rosmarinic acid (RA) is a phenolic antioxidant naturally occurring in the plants of the Lamiaceae family, including basil (Ocimum basilicum L.). Existing analytical methods for determining the RA content in leaves are time-consuming and destructive, posing limitations on quality assessment and control during cultivation. In this study, we aimed to develop non-destructive prediction models for the RA content in basil plants using a portable hyperspectral imaging (HSI) system and machine learning algorithms. The basil plants were grown in a vertical farm module with controlled environments, and the HSI of the whole plant was captured using a portable HSI camera in the range of 400–850 nm. The average spectra were extracted from the segmented regions of the plants. We employed several spectral data pre-processing methods and ensemble learning algorithms, such as Random Forest, AdaBoost, XGBoost, and LightGBM, to develop the RA prediction model and feature selection based on feature importance. The best RA prediction model was the LightGBM model with feature selection by the AdaBoost algorithm and spectral pre-processing through logarithmic transformation and second derivative. This model performed satisfactorily for practical screening with R2P = 0.81 and RMSEP = 3.92. From in-field HSI data, the developed model successfully estimated and visualized the RA distribution in basil plants growing in the greenhouse. Our findings demonstrate the potential use of a portable HSI system for monitoring and controlling pharmaceutical quality in medicinal plants during cultivation. This non-destructive and rapid method can provide a valuable tool for assessing the quality of RA in basil plants, thereby enhancing the efficiency and accuracy of quality control during the cultivation stage.

Geographical origin differentiation and quality determination of saffron using a portable Hyperspectral imaging system

A portable Hyperspectral Imaging (HSI) system (400–1000 nm) coupled with chemometrics techniques was developed to identify the geographical origin and to determine the Crocins content of saffron rapidly and non-destructively. Reflectance spectra of 160 saffron samples from two geographical origins were captured and preprocessed using the Savitzky–Golay (SG) algorithm. PCA as an unsupervised clustering and PLS-DA, RBF, and MLP as supervised techniques were applied for the classification and geographical origin differentiation of the samples. Afterward, the sample’s Crocins content was measured and their associations with their spectra were modeled using PLSR, RBF, and MLP models. The PCA results indicated that saffron samples were originally distributed in two different clusters. PLS-DA model with a spectral range from 400 to 1000 nm achieved the best clustering performance (Sensitivity and Specificity: 100 %). The RBF model showed good predictive capabilities in the saffron Crocins content prediction and quality evaluation (R2cal > 0.94 and R2pred > 0.83).

Practical snapshot hyperspectral imaging with DOE

As the pursuit of snapshot spectral imaging continued to grow, traditional hyperspectral imaging systems have been too enormous and too slow to implement in real scenarios. Considering the portability and the demand for snapshots, in this study, we proposed a practical hyperspectral camera with a designed diffractive optical element (DOE). The designed DOE distinguished the incident spectrums and converged them into different point spread functions (PSFs) on the imaging plane. Utilizing the spectrally-varying PSF information, we engaged the iterative algorithm with the deep-learning model to reconstruct hyperspectral data. Experimental results demonstrated that the proposed system performed at least as well as the current methods and could achieve great spatial resolution and spectral accuracy in spectral imaging. This proposed system had good potential in portable hyperspectral imaging system.

Use of a portable hyperspectral imaging system for monitoring the efficacy of sanitation procedures in produce processing plants

Cleaning and sanitation of production surfaces and equipment plays a critical role in lowering the risk of food borne illness associated with consumption of fresh-cut produce. Visual observation and sampling methods including ATP tests and cell culturing are commonly used to monitor the effectiveness of the cleaning procedures. This study tested the ability of a hand-held VIS hyperspectral imaging system to augment current monitoring methods. Multiple visits were made to two commercial fresh-cut processing facilities. Fluorescence-based detection and automated cycling among three wavelengths, 475, 520, and 675nm, proved best for detecting a range of anomalies. Numerous deficiencies in existing cleaning protocols were identified. Plant personnel were able to devise changes in the protocols that eliminated most of the detected problems without increasing the net expenditure for cleaning efforts. In addition, imaging identified sites where ATP test results were higher when compared to results for adjacent areas.

Development of a portable hyperspectral imaging system for monitoring the efficacy of sanitation procedures in food processing facilities

Cleaning and sanitation in food processing facilities is a critical step in reducing the risk of transfer of pathogenic organisms to food consumed by the public. Current methods to check the effectiveness of sanitation procedures rely on visual observation and sub-sampling tests such as ATP bioluminescence assays and culturing methods. To augment existing verification methods, a hand-held visible hyperspectral imaging device was developed. The device is capable of acquiring reflectance images using ambient lighting, and fluorescence responses to supplemental violet (405nm) excitation. To enhance the ability of detecting relatively low intensity fluorescence responses in the presence of ambient lighting, the device includes the ability to identify wavebands where the intensity of ambient lighting is relatively low. Valleys in ambient illumination intensity when using fluorescent lighting were found at around 475, 520, 570, and 675nm. A principal goal is to acquire data to support development of a commercially-viable, hand-held, imaging system.

Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method

Citrus canker is one of the most devastating diseases that threaten marketability of citrus crops. Technologies that can efficiently identify citrus canker would assure fruit quality and safety and enhance the competitiveness and profitability of the citrus industry. This research was aimed to investigate the potential of using hyperspectral imaging technique for detecting canker lesions on citrus fruit. A portable hyperspectral imaging system consisting of an automatic sample handling unit, a light source, and a hyperspectral imaging unit was developed for citrus canker detection. The imaging system was used to acquire reflectance images from citrus samples in the wavelength range between 400 and 900 nm. Ruby Red grapefruits with normal and various diseased skin conditions including canker, copper burn, greasy spot, wind scar, cake melanose, and specular melanose were tested. Hyperspectral reflectance images were analyzed using principal component analysis (PCA) to compress the 3-D hyperspectral image data and extract useful image features that could be used to discriminate cankerous samples from normal and other diseased samples. Image processing and classification algorithms were developed based upon the transformed images of PCA. The overall accuracy for canker detection was 92.7%. Four optimal wavelengths (553, 677, 718, and 858 nm) were identified in visible and short-wavelength near-infrared region that could be adopted by a future multispectral imaging solution for detecting citrus canker on a sorting machine. This research demonstrated that hyperspectral imaging technique could be used for discriminating citrus canker from other confounding diseases.

Plant classification for weed detection using hyperspectral imaging with wavelet analysis

The goal of this study is to develop a new weed detection method that can be applied for automatic mechanical weed control. For successful weed detection, plants must be classified into crops and weeds according to their species. In this study, we employed a portable hyperspectral imaging system. The hyperspectral camera can capture landscape images that include crops, weeds, and the soil surface, and can provide more extensive information than conventional red, green, and blue (RGB) images. Although RGB images consist of red, green, and blue wavebands, the obtained hyperspectral images consist of 240 wavebands of spectral information. Hyperspectral imaging is expected to provide powerful technology for agricultural sensing. In the initial step of this study, the image pixels of the plants (crop or weeds) were segmented from the background soil surface using Euclidean distance as the discriminant function. In the next step, the image pixels of the crop (sugarbeet) and weeds (four species) were classified using the difference in the spectral characteristics of the plant species. In this process, classification variables were generated using wavelet transformation for data compression, noise reduction, and feature extraction, and then stepwise linear discriminant analysis was applied. The validation results indicate that the developed classification method has potential for practical use.

Monitoring Spatio-temporal Dynamics of Photosynthesis with a Portable Hyperspectral Imaging System

Photosynthetic efficiency of higher plants dynamically adapts to changing light intensity and is greatly influenced by stress, such as water stress. We tested a new portable hyperspectral imaging system, the SOC-700, manufactured by Surface Optics, which produces 12-bit reflectance images between 440 nm and 880 nm with a 4 nm spectral resolution. We quantified the reflectance properties and photochemical reflectance index (PRI) during light adaptation of genetically modified Arabidopsis thaliana (L.) Heynh. plants lacking or over-expressing the PsbS protein, an essential component of the mechanism of non-photochemical dissipation. In a second experiment, PRI images of gradually water stressed leaves were compared to leaf-level measurements of reflectance using a second commercially available spectrometer, and chlorophyll fluorescence to detect dynamic, photosynthesis correlated changes in reflectance and PRI. In both experiments PRI measured with the SOC-700 changed, reflecting the biochemical adaptation of the photosynthetic apparatus to high light intensity (dynamic changes within minutes) and the gradual deactivation of photosynthesis during drying (changes within hours). The quantum efficiency of photosystem II (� F/Fm� ) and non-photochemical energy dissipation (NPQ) measured from chlorophyll fluorescence, were strongly correlated with PRI. Leaf area PRI values estimated from individual pixel spectra of the SOC-700 quantified photosynthetic efficiency more thoroughly than PRI values calculated from point measurements using the hand-held GER-1500. The applications, limitations, and potential of the SOC-700 for plant eco-physiology and remote sensing are also discussed.