Identification of Chilling Injury in Kiwifruit Using Hyperspectral Structured-Illumination Reflectance Imaging System (SIRI) with Support Vector Machine (SVM) Modelling

Ge, Yonghui; Tu, Siying

doi:10.1080/00032719.2022.2153364

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…They were used to compensate for additive and/or multiplicative effects in spectral data. Studies on different subjects showed different performances of each scatter-correction technique [45,46]. Our result showed improved sweetness prediction results after MSC and SNV transformation than the smoothed spectra.…”

Section: Impact Of Spectral Pre-processing On Sweetness Predictionmentioning

confidence: 70%

Prediction of Kiwifruit Sweetness with Vis/NIR Spectroscopy Based on Scatter Correction and Feature Selection Techniques

Wan,

Yue,

et al. 2024

Applied Sciences

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The sweetness is an important parameter for the quality of Cuixiang kiwifruit. The quick and accurate assessment of sweetness is necessary for farmers to make timely orchard management and for consumers to make purchasing choices. The objective of the study was to propose an effective physical method for determining the sweetness of fresh kiwifruit based on fruit hyperspectral reflectance in 400–2500 nm. In this study, the visible and near-infrared spectral (Vis/NIR) reflectance and sweetness values of kiwifruit were measured at different time periods after the fruit matured in 2021 and 2022. The multiplicative scatter correction (MSC) and standard normal variable (SNV) transformation were used for spectral denoising. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) methods were employed to select the most effective features for sweetness, and then the features were used as the inputs of partial least squares (PLS), least squares support vector machine (LSSVM), back propagation neural network (BP), and multiple linear regression (MLR) models to explore the best way of sweetness predicting. The study indicated that the most sensitive features were in the blue and red regions and the 970, 1200, and 1400 nm. The sweetness estimation model constructed by using the data of the whole harvest period from August to October performed better than the models constructed by each harvest period. Overall results indicated that hyperspectral reflectance incorporated with MSC-SPA-LSSVM could explain up to 79% of the variability in kiwifruit sweetness, which could be applied as an alternative fast and accurate method for the non-destructive determination of the sweetness of kiwifruit. This research could partially provide a theoretical basis for the development of nondestructive instrumentation for the detection of kiwifruit sweetness.

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Section: Impact Of Spectral Pre-processing On Sweetness Predictionmentioning

confidence: 70%

Prediction of Kiwifruit Sweetness with Vis/NIR Spectroscopy Based on Scatter Correction and Feature Selection Techniques

Wan,

Yue,

et al. 2024

Applied Sciences

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“…Therefore, using high-throughput methods which can accurately and efficiently measure fruit and vegetable quality attributes is essential. Chilling injury, one of the most common postharvest physiological disorder in horticultural products was detected using hyperspectral imaging and achieved more than 91% detection accuracy in apple, peach, and kiwi fruit [ 93 , 94 , 95 , 96 ].…”

Section: Applications Of Image-based High-throughput Phenotyping In H...mentioning

confidence: 99%

Image-Based High-Throughput Phenotyping in Horticultural Crops

Abebe

Kim

et al. 2023

Plants

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Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

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“…For example, spectral reflectance in the wavelength regions of 500-600 nm, 650-700 nm, and 800-850 nm was used to predict the sunscald grade of 'Packham's Triumph' pears with accuracy of 94% [10]; a prediction model for apple sunscald used VIS-NIR reflectance data to determine the effect of predicting apple sunscald in advance [20]. To study the environmental stress of kiwifruit, Ge used spectral technology and SVM to detect chilling injuries in kiwifruit in 2023 and achieved 94.2% accuracy [21]. However, research on how to use hyperspectral data to detect kiwifruit sunscald is still scarce.…”

Section: Introductionmentioning

confidence: 99%

The Detection of Kiwifruit Sunscald Using Spectral Reflectance Data Combined with Machine Learning and CNNs

Jia

Duan

2023

Agronomy

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Sunscald in kiwifruit, an environmental stress caused by solar radiation during the summer, reduces fruit quality and yields and causes economic losses. The efficient and timely detection of sunscald and similar diseases is a challenging task but helps to implement measures to control stress. This study provides high-precision detection models and relevant spectral information on kiwifruit physiology for similar statuses, including early-stage sunscald, late-stage sunscald, anthracnose, and healthy. Primarily, in the laboratory, 429 groups of spectral reflectance data for leaves of four statuses were collected and analyzed using a hyperspectral reflection acquisition system. Then, multiple modeling approaches, including combined preprocessing methods, feature extraction algorithms, and classification algorithms, were designed to extract bands and evaluate the performance of the models to detect the statuses of kiwifruit. Finally, the detection of different stages of kiwifruit sunscald under anthracnose interference was accomplished. As influential bands, 694–713 nm, 758–777 nm, 780–799 nm, and 1303–1322 nm were extracted. The overall accuracy, precision, recall, and F1-score values of the models reached 100%, demonstrating an ability to detect all statuses with 100% accuracy. It was concluded that the combined processing of moving average and standard normal variable transformations (MS) could significantly improve the data; the near-infrared support vector machine and visible convolutional neural network with MS (NIR-MS-SVM and VIS-MS-CNN) were established as high-precision detection techniques for the classification of similar kiwifruit statuses, demonstrating 25.58% higher accuracy than the single support vector machine. The VIS-MS-CNN model reached convergence with a stable cross-entropy loss of 0.75 in training and 0.77 in validation. The techniques developed in this study will improve orchard management efficiency and yields and increase researchers’ understanding of kiwifruit physiology.

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Identification of Chilling Injury in Kiwifruit Using Hyperspectral Structured-Illumination Reflectance Imaging System (SIRI) with Support Vector Machine (SVM) Modelling

Cited by 6 publications

References 26 publications

Prediction of Kiwifruit Sweetness with Vis/NIR Spectroscopy Based on Scatter Correction and Feature Selection Techniques

Prediction of Kiwifruit Sweetness with Vis/NIR Spectroscopy Based on Scatter Correction and Feature Selection Techniques

Image-Based High-Throughput Phenotyping in Horticultural Crops

The Detection of Kiwifruit Sunscald Using Spectral Reflectance Data Combined with Machine Learning and CNNs

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