Phase based Time Resolved Reflectance Spectroscopy using Time-of-Flight Camera for Fruit Quality Monitoring
09 Mar 2020-pp 1-6
TL;DR: In this article, a time-resolved phase-based reflectance spectroscopy was used for the nondestructive assessment of the internal quality of the fruits in this work.
Abstract: Time-resolved phase-based reflectance spectroscopy has been used for the nondestructive assessment of the internal quality of the fruits in this work. The time taken by the incident light to backscatter and reach the detector placed at a distance from the camera relates to the penetration depth or mean free path of the light into the apples. The mean free path changes due to chemical pigmentation changes in apples during storage and ripening. Therefore, the scattering characteristics changes. The phase changes in the backscattered light are correlated to the stage of ripening or storage. The degree of linear polarization is used to quantify the changes in the reflected phase with increasing storage time. The proposed optical instrumentation is simple in construction and computationally less intensive and therefore, provides an affordable solution for a handheld compact fruit monitoring device.
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TL;DR: In this article , the performance of five depth cameras in relation to their potential for grape yield estimation was investigated, including their performance in and out of direct sunlight, their ability to accurately measure the shape of the grapes, and their potential to facilitate counting and sizing of individual berries.
Abstract: This work investigates the performance of five depth cameras in relation to their potential for grape yield estimation. The technologies used by these cameras include structured light (Kinect V1), active infrared stereoscopy (RealSense D415), time of flight (Kinect V2 and Kinect Azure), and LiDAR (Intel L515). To evaluate their suitability for grape yield estimation, a range of factors were investigated including their performance in and out of direct sunlight, their ability to accurately measure the shape of the grapes, and their potential to facilitate counting and sizing of individual berries. The depth cameras’ performance was benchmarked using high-resolution photogrammetry scans. All the cameras except the Kinect V1 were able to operate in direct sunlight. Indoors, the RealSense D415 camera provided the most accurate depth scans of grape bunches, with a 2 mm average depth error relative to photogrammetric scans. However, its performance was reduced in direct sunlight. The time of flight and LiDAR cameras provided depth scans of grapes that had about an 8 mm depth bias. Furthermore, the individual berries manifested in the scans as pointed shape distortions. This led to an underestimation of berry sizes when applying the RANSAC sphere fitting but may help with the detection of individual berries with more advanced algorithms. Applying an opaque coating to the surface of the grapes reduced the observed distance bias and shape distortion. This indicated that these are likely caused by the cameras’ transmitted light experiencing diffused scattering within the grapes. More work is needed to investigate if this distortion can be used for enhanced measurement of grape properties such as ripeness and berry size.
4 citations
01 Jan 2022
TL;DR: In this article , a combination of deep learning, molecular analysis using advanced techniques such as chromatography, electrophoresis and spectroscopy, and genome characterization will constitute a novel approach for probing the quality dynamics of food substances.
Abstract: Assessment of food quality is an important feature in novel food products development process. This is because quality has implications for safety, nutritional contents, traceability, and market value of foods. However, due to the biochemical complexities of food products, it has become important that advanced analytical tools relating to proteomics, genomics, and big data be used to completely characterize the molecular features of food substances and monitor potential variations in quality. Such in silico tools could be used to generate molecular templates for probing the biochemical and chemo-molecular features of food substances to validate food quality for high throughput screening. For example, deep learning has received significant attention due to its capacity for feature learning based on multi-layer artificial neural networks. The combination of deep learning, molecular analysis using advanced techniques such as chromatography, electrophoresis and spectroscopy, and genome characterization will constitute a novel approach for probing the quality dynamics of food substances. Discussed in this chapter are opportunities for integrated chemical analysis, bioinformatics, and computational approaches for effective monitoring of food quality. In addition, current advancements in food quality monitoring through a combination of proteomic and big data tools, as well as their future perspectives are addressed.
2 citations
30 Apr 2021
TL;DR: A prospective view of how much calories is consumed by a person before and after a meal is presented, and a deep learning system to monitor the nutrient intake for hospitalized patients is proposed.
Abstract: This paper presents a prospective view of how much calories is consumed by a person before and after a meal. From the calorie intake project, the management gets to know whether the person consumed the necessary amount of calorie. Although the hospital diet can provide adequate energy and nutrients, many patients may not consume sufficient food to meet their needs. The estimated energy intake of about one-third of patients was very low, and vitamin C, calcium and zinc intakes were also of concern. At least one-third of patients arriving at the hospital are malnourished. This can mean a patient’s diet does not provide the right amount or appropriate ratio of calories, macronutrients, or micronutrients to promote adequate healing. If this continuous then it may be a risk to the patient’s health and may not be a good sign. In this project, we proposed a deep learning system to monitor the nutrient intake for hospitalized patients. A Camera will scan the amount of food given to the patient, when the patient finishes the meal again the camera will again scan the amount of food the patient has taken. It will also note down the number of calories the patient has intake and the amount of calories the patient has left. This will help hospitalized patient to be healthy and will also help hospital staffs to monitor with ease.
References
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TL;DR: An overview of NIR spectroscopy for measuring quality attributes of horticultural produce is given in this article, where the problem of calibration transfer from one spectrophotometer to another is introduced as well as techniques for calibration transfer.
1,780 citations
TL;DR: A detailed overview of the comparative introduction, latest developments and applications of computer vision systems in the external quality inspection of fruits and vegetables is presented.
319 citations
TL;DR: In this paper, a spatially resolved steady-state diffuse reflectance technique was used to measure the optical properties of fresh fruits and vegetables over the visible and short-wave near-infrared region (500-1000 nm).
300 citations
"Phase based Time Resolved Reflectan..." refers background in this paper
...In [7] a novel spatially resolved hyperspectral diffuse reflectance imaging system is proposed to obtain the optical properties of fruits and vegetables....
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TL;DR: In this article, the authors used the temporal dispersion of a short laser pulse injected into the probed medium to measure the mean values of the absorption and reduced scattering coefficients of the medium.
Abstract: Time-resolved reflectance has been used for the nondestructive measurement of optical properties in apples. The technique is based on the detection of the temporal dispersion of a short laser pulse injected into the probed medium. The time distribution of re-emitted photons interpreted with a solution of the diffusion equation yields the mean values of the absorption and reduced scattering coefficients of the medium. The proposed technique proved useful for the measurement of the absorption and scattering spectra of different varieties of apples, revealing the spectral shape of chlorophyll. No major variations were observed in the experimental data when the fruit was peeled, showing that the optical properties measured were those of the pulp. With this technique the change in chlorophyll absorption during storage and ripening could be followed. Finally, a compact prototype working at few selected wavelengths was designed and constructed, demonstrating potentialities of the technique for industrial applications.
106 citations
"Phase based Time Resolved Reflectan..." refers background in this paper
...For non-invasive and non-destructive quality monitoring of the internal pulp of fruits, time-resolved reflectance spectroscopy (TRS) is proposed in [9]....
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TL;DR: In this article, two non-destructive dynamic test methods, low-mass impact and acoustic response, were tested and compared with destructive compression and penetration tests to evaluate apple firmness.
102 citations
"Phase based Time Resolved Reflectan..." refers methods in this paper
...In [3], accoustic measurement are used to measure the tissue elasticity which relates to the softening of the apples during storage....
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