As it is high in value, extra virgin olive oil (EVOO) is frequently blended with inferior vegetable oils. This study presents an optical method for determining the adulteration level of EVOO with soybean oil as well as peanut oil using LED-induced fluorescence spectroscopy. Eight LEDs with central wavelengths from ultra-violet (UV) to blue are tested to induce the fluorescence spectra of EVOO, peanut oil, and soybean oil, and the UV LED of 372 nm is selected for further detection. Samples are prepared by mixing olive oil with different volume fractions of peanut or soybean oil, and their fluorescence spectra are collected. Different pre-processing and regression methods are utilized to build the prediction model, and good linearity is obtained between the predicted and actual adulteration concentration. This result, accompanied by the non-destruction and no pre-treatment characteristics, proves that it is feasible to use LED-induced fluorescence spectroscopy as a way to investigate the EVOO adulteration level, and paves the way for building a hand-hold device that can be applied to real market conditions in the future.

This work proposes a novel fluorescence Scheimpflug LiDAR (SLiDAR) technique based on the Scheimpflug principle for three-dimension (3D) plant profile measurements. A 405 nm laser diode was employed as the excitation light source to generate a light sheet. Both the elastic and inelastic/fluorescence signals from a target object (e.g., plants) can be simultaneously measured by the fluorescence SLiDAR system employing a color image sensor with blue, green and red detection channels. The 3D profile can be obtained from the elastic signal recorded by blue pixels through elevation scanning measurements, while the fluorescence intensity of the target object is mainly acquired by red and green pixels. The normalized fluorescence intensity of the red channel, related to the chlorophyll distribution of the plant, can be utilized for the classification of leaves, branches and trunks. The promising results demonstrated in this work have shown a great potential of employing the fluorescence SLiDAR technique for 3D fluorescence profiling of plants in agriculture and forestry applications.

The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.

A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.

The in vivo features of the absorption of leaf photosynthetic and photo-protective pigments are closely linked to the leaf spectrum in the 400–800 nm regions. However, this information is difficult to obtain because the overlapping leaf pigments can mask the contribution of individual pigments to the leaf spectrum. Here, to limit the masking phenomenon between these pigments, the separation technology for leaf spectral overlapping was employed in the PROSPECT model with the ZJU dataset. The main results of this study include the following aspects: (1) the absorption coefficients of separated chlorophyll a and b, carotenoids and anthocyanins in the leaf in vivo display the physical principles of forming an absorption spectrum similar to those in an organic solution; (2) the differences in the position of each absorption peak of pigments between the leaf in vivo and in an organic solution can be described by a spectral displacement parameter; and (3) the overlapping characteristics between the separated pigments in the leaf in vivo are clearly drawn by a range of absorption feature (RAF) parameter. Moreover, the absorption coefficients of the separated pigments were successfully applied in leaf spectral modeling and pigment retrieval. The results show that the separated multiple pigment absorption coefficients from the leaf spectrum in vivo are effective and provide a framework for future refinements in describing leaf optical properties.

The accelerating development of high-throughput plant phenotyping demands a LiDAR system to achieve spectral point cloud, which will significantly improve the accuracy and efficiency of segmentation based on its intrinsic fusion of spectral and spatial data. Meanwhile, a relatively longer detection range is required for platforms e.g., unmanned aerial vehicles (UAV) and poles. Towards the aims above, what we believe to be, a novel multispectral fluorescence LiDAR, featuring compact volume, light weight, and low cost, has been proposed and designed. A 405 nm laser diode was employed to excite the fluorescence of plants, and the point cloud attached with both the elastic and inelastic signal intensities that was obtained through the R-, G-, B-channels of a color image sensor. A new position retrieval method has been developed to evaluate far field echo signals, from which the spectral point cloud can be obtained. Experiments were designed to validate the spectral/spatial accuracy and the segmentation performance. It has been found out that the values obtained through the R-, G-, B-channels are consistent with the emission spectrum measured by a spectrometer, achieving a maximum R² of 0.97. The theoretical spatial resolution can reach up to 47 mm and 0.7 mm in the x- and y-direction at a distance of around 30 m, respectively. The values of recall, precision, and F score for the segmentation of the fluorescence point cloud were all beyond 0.97. Besides, a field test has been carried out on plants at a distance of about 26 m, which further demonstrated that the multispectral fluorescence data can significantly facilitate the segmentation process in a complex scene. These promising results prove that the proposed multispectral fluorescence LiDAR has great potential in applications of digital forestry inventory and intelligent agriculture.