Estimation of the sugar content of fruit by energy-resolved computed tomography using a material decomposition method

Kanno, Ikuo; Kuroyama, Takahiro

doi:10.1080/00223131.2020.1845836

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…The photon-counting micro-CT system, as demonstrated in our study, holds significant potential for advancing plant phenomics research. Its ability for material decomposition, exemplified by the quantification of sucrose ( Kanno and Kuroyama, 2021 ), organic acids, vitamins and important minerals compounds, introduces an innovative dimension in plant biology for the analysis of internal compositions and phenotypic attributes. It could also transform the way we understand root systems ( Mooney et al., 2012 ), seed germination, and plant-microorganism interactions, providing insights into plant growth, disease resistance, and nutrient uptake.…”

Section: Discussionmentioning

confidence: 99%

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

Xue,

Huang,

et al. 2024

Front. Plant Sci.

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IntroductionIn contemporary agronomic research, the focus has increasingly shifted towards non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors.MethodsFor automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed. Machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species.ResultsThese models demonstrate outstanding performance with Dice, Recall, and Precision indices predominantly ranging between 0.90 and 0.99. The Mean Intersection over Union (MIoU) consistently falls between 0.88 and 0.98. This approach served both as a feature selection process and a means of classification, significantly enhancing the study's ability to discern and categorize distinct lychee varieties.DiscussionThis research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species.

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Section: Discussionmentioning

confidence: 99%

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

Xue,

Huang,

et al. 2024

Front. Plant Sci.

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“…However, none of these techniques can provide detailed information about the internal tissues of the fruit while also predicting its biophysical and biochemical characteristics efficiently. Although similar works approached the reconstruction of fruits using other techniques, such as electrical impedance to detect the tomato level of maturation (Verma et al, 2021), computed tomography in detect bitter pit in apples (Si and Sankaran, 2016) and X-ray in predicting the sugar content in kiwi fruit (Kanno and Kuroyama, 2020) and phenotyping charcteristics of seed in predicting the seed length, width, thickness, and radius of soybean and wheat with an accuracy ranging 80-96% (Liu et al, 2020), some of these techniques are expensive and not expedite for in situ measurements. Additionally, these methods are mainly used to identify physical damage, and none of them analysed each tissue individually or presented a qualitative approach (Donis-Gonzaĺez et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition

Tosin,

Cunha,

Monteiro-Silva

et al. 2024

Front. Plant Sci.

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IntroductionPrecision monitoring maturity in climacteric fruits like tomato is crucial for minimising losses within the food supply chain and enhancing pre- and post-harvest production and utilisation.ObjectivesThis paper introduces an approach to analyse the precision maturation of tomato using hyperspectral tomography-like.MethodsA novel bi-directional spectral reconstruction method is presented, leveraging visible to near-infrared (Vis-NIR) information gathered from tomato spectra and their internal tissues (skin, pulp, and seeds). The study, encompassing 118 tomatoes at various maturation stages, employs a multi-block hierarchical principal component analysis combined with partial least squares for bi-directional reconstruction. The approach involves predicting internal tissue spectra by decomposing the overall tomato spectral information, creating a superset with eight latent variables for each tissue. The reverse process also utilises eight latent variables for reconstructing skin, pulp, and seed spectral data.ResultsThe reconstruction of the tomato spectra presents a mean absolute percentage error of 30.44 % and 5.37 %, 5.25 % and 6.42 % and Pearson’s correlation coefficient of 0.85, 0.98, 0.99 and 0.99 for the skin, pulp and seed, respectively. Quality parameters, including soluble solid content (%), chlorophyll (a.u.), lycopene (a.u.), and puncture force (N), were assessed and modelled with PLS with the original and reconstructed datasets, presenting a range of R2 higher than 0.84 in the reconstructed dataset. An empirical demonstration of the tomato maturation in the internal tissues revealed the dynamic of the chlorophyll and lycopene in the different tissues during the maturation process.ConclusionThe proposed approach for inner tomato tissue spectral inference is highly reliable, provides early indications and is easy to operate. This study highlights the potential of Vis-NIR devices in precision fruit maturation assessment, surpassing conventional labour-intensive techniques in cost-effectiveness and efficiency. The implications of this advancement extend to various agronomic and food chain applications, promising substantial improvements in monitoring and enhancing fruit quality.

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“…Look-up tables (LUTs) are then prepared by interpolating the currents. Previously, the LUTs were estimated by calculations using response functions [11]. Then, the ER-CT measurements are performed for a phantom consisting of various resins.…”

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confidence: 99%

Energy-resolved computed tomography with a response-function-less transXend detector

Kanno

Yamada

2021

Journal of Nuclear Science and Technology

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https://repository.kulib.kyoto-u.ac.jp Energy-resolved computed tomography with a response-function-less transXend detectorA "transXend" detector measures X-rays as electric current and yields an X-ray energy spectrum after analysis. Response functions are necessary for the analysis and are previously estimated using material details of the X-ray detector. The detector specifics, however, are not always available to users. Here, a "responsefunction-less" transXend detector is described that relies on transmission measurements of X-rays that passed through known basis materials. The validity of the method is verified by obtaining effective atomic numbers for nine resins.

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Estimation of the sugar content of fruit by energy-resolved computed tomography using a material decomposition method

Cited by 9 publications

References 17 publications

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition

Energy-resolved computed tomography with a response-function-less transXend detector

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