Magnetic Resonance Imaging for Quality Evaluation of Fruits: a Review

Srivastava, Ragini; Talluri, Sekhar; Beebi, Sk. Khasim; Kumar, Bindey

doi:10.1007/s12161-018-1262-6

Cited by 32 publications

(9 citation statements)

References 140 publications

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“…The relaxation times can be utilized to assess the fruit-ripening stage, fruit firmness, as those are sensitive to mobility. A time-domain analysis is used in the study of relaxation and diffusion data [ 66 ]. Table 5 summarises the use of relaxation time for different applications reported in various research.…”

Section: Nondestructive Methods For Brix Measurement In Fruits and Ve...mentioning

confidence: 99%

“…Even though NMR spectroscopy and MRI have been proved to be an effective nondestructive method for the quality evaluation of fruits, they have found only limited applications in modern industrial and commercial applications. The size, weight, cost, and stability of magnets and the throughput are the major constraints for the wider application of MR-based technology [ 66 ]. The specifications of nondestructive Brix sensors are summarized in Table 6 .…”

Section: Nondestructive Methods For Brix Measurement In Fruits and Ve...mentioning

confidence: 99%

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Sensors and Instruments for Brix Measurement: A Review

Jaywant

Singh

Arif

2022

Sensors

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Quality assessment of fruits, vegetables, or beverages involves classifying the products according to the quality traits such as, appearance, texture, flavor, sugar content. The measurement of sugar content, or Brix, as it is commonly known, is an essential part of the quality analysis of the agricultural products and alcoholic beverages. The Brix monitoring of fruit and vegetables by destructive methods includes sensory assessment involving sensory panels, instruments such as refractometer, hydrometer, and liquid chromatography. However, these techniques are manual, time-consuming, and most importantly, the fruits or vegetables are damaged during testing. On the other hand, the traditional sample-based methods involve manual sample collection of the liquid from the tank in fruit/vegetable juice making and in wineries or breweries. Labour ineffectiveness can be a significant drawback of such methods. This review presents recent developments in different destructive and nondestructive Brix measurement techniques focused on fruits, vegetables, and beverages. It is concluded that while there exist a variety of methods and instruments for Brix measurement, traits such as promptness and low cost of analysis, minimal sample preparation, and environmental friendliness are still among the prime requirements of the industry.

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Section: Nondestructive Methods For Brix Measurement In Fruits and Ve...mentioning

confidence: 99%

Section: Nondestructive Methods For Brix Measurement In Fruits and Ve...mentioning

confidence: 99%

Sensors and Instruments for Brix Measurement: A Review

Jaywant

Singh

Arif

2022

Sensors

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“…In contrast, non-destructive methods have the advantages of lower cost, more reliable detection results, and no need to destroy the fruit ( Arunkumar et al, 2021 ). Various non-destructive detecting methods for fruit maturity have been extensively studied, such as ultrasonic methods ( Yildiz et al, 2018 ), near-infrared spectroscopy ( Pissard et al, 2021 ), scanning laser Doppler vibrometers ( Hosoya et al, 2017 ), magnetic resonance imaging ( Srivastava et al, 2018 ), electronic nose ( Calvini and Pigani, 2022 ), and so on. Although these methods have high detection accuracy, they are only suitable for a single fruit or laboratory detection.…”

Section: Introductionmentioning

confidence: 99%

Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

et al. 2022

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The fast and precise detection of dense litchi fruits and the determination of their maturity is of great practical significance for yield estimation in litchi orchards and robot harvesting. Factors such as complex growth environment, dense distribution, and random occlusion by leaves, branches, and other litchi fruits easily cause the predicted output based on computer vision deviate from the actual value. This study proposed a fast and precise litchi fruit detection method and application software based on an improved You Only Look Once version 5 (YOLOv5) model, which can be used for the detection and yield estimation of litchi in orchards. First, a dataset of litchi with different maturity levels was established. Second, the YOLOv5s model was chosen as a base version of the improved model. ShuffleNet v2 was used as the improved backbone network, and then the backbone network was fine-tuned to simplify the model structure. In the feature fusion stage, the CBAM module was introduced to further refine litchi’s effective feature information. Considering the characteristics of the small size of dense litchi fruits, the 1,280 × 1,280 was used as the improved model input size while we optimized the network structure. To evaluate the performance of the proposed method, we performed ablation experiments and compared it with other models on the test set. The results showed that the improved model’s mean average precision (mAP) presented a 3.5% improvement and 62.77% compression in model size compared with the original model. The improved model size is 5.1 MB, and the frame per second (FPS) is 78.13 frames/s at a confidence of 0.5. The model performs well in precision and robustness in different scenarios. In addition, we developed an Android application for litchi counting and yield estimation based on the improved model. It is known from the experiment that the correlation coefficient R2 between the application test and the actual results was 0.9879. In summary, our improved method achieves high precision, lightweight, and fast detection performance at large scales. The method can provide technical means for portable yield estimation and visual recognition of litchi harvesting robots.

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“…Magnetic resonance imaging (MRI) uses a strong magnetic field, radio waves and a computer to produce detailed images of the nucleuses in the target and make it possible to gain a deep insight into the real damages inside the agricultural product [6,7]. Zhang et al used the MRI technology to observe the ripening of tomatoes.…”

Section: Introductionmentioning

confidence: 99%

Assessment and Feasibility Study of Lemon Ripening Using X-ray Image of Information Visualization

et al. 2021

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Digital radiography (DR) is a mature technology and has been broadly used in medical diagnosis. Currently, it’s also used for fruit quality inspection in the market. This purpose of the study is to conduct non-destructive experiments for visual comparisons of digital radiography images, further construct visualized grayscale image analysis technology, and analyze the changes in lemon quality and ripening using quantitative statistical methods. The materials used for the experiments were three lemons of different ripening. A general medical X-ray DR system for was used in this study for 2D digital radiography. The medical X-ray DR images were created based on the Digital Imaging and Communications in Medicine (DICOM) standard. Photometric interpretation of monochrome was applied to create multi-layered grayscale images. Then quantitative analyses and comparisons were performed with image matrix structures and grayscale pixel values in the tissues using visualization techniques and statistical methods. After layer segmentation on the radiological images, the correlations between the lemon structures and tissue changes were assessed by using the Kruskal–Wallis test. The results showed that the p values for lemon, fiber, and pulp were all under 0.05, while the peel layer did not exhibit significant change. The pulp layer is the best region for statistical analyses to determine the lemon ripening. In conclusion, this study can provide a solid reference for future quality classification in the agricultural market. The research findings can be referenced for developing computing techniques applied to agricultural inspection, expanding the scope of application of the medical DR technology.

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Magnetic Resonance Imaging for Quality Evaluation of Fruits: a Review

Cited by 32 publications

References 140 publications

Sensors and Instruments for Brix Measurement: A Review

Sensors and Instruments for Brix Measurement: A Review

Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

Assessment and Feasibility Study of Lemon Ripening Using X-ray Image of Information Visualization

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