Ukrainian Journal of Physical Optics

2022 Volume 23, Issue 4

ISSN 1816-2002 (Online), ISSN 1609-1833 (Print)

Hazelnut quality detection based on deep learning and near-infrared spectroscopy

Dandan Li, Dongyan Zhang, Dapeng Jiang, Jun Cao and Jiuqing Liu

College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, Heilongjiang, China


Hazelnut kernels can often be incomplete or malformed and have some other defects. Manual methods for their classification and recognition or even old-fashioned machine-learning approaches are prone to the problems of low recognition efficiency and high misjudgement rates. In this study we apply deep learning to the problem of recognition of hazelnut-kernel defects. Two convolution neural-network systems, MobileNetV2 and Resnet-50, are used for training and recognition. It is found that the prediction accuracy of the ResNet-50 training set is improved by 10.3% and the training-loss rate reduced by 0.041, if compared with MobileNetV2. Moreover, the validation accuracy achieved with ResNet-50 is higher by 13.9% and the validation-loss rate lower by 0.151. This proves that the overall training effect of the Resnet-50 neural network is better than that of MobileNetV2. Basing on the near-infrared absorption spectroscopy, we also detect the protein content in hazelnuts, which represents an important parameter for evaluating their quality. A Kennard–Stone algorithm is used to classify a sample set. To elaborate a technique for the quantitative protein analysis of hazelnuts, we employ a partial least-squares method. The appropriate spectral data is preprocessed according to the methods of first derivative, second derivative and standard normal variate. The effect of these methods on the accuracy are compared. The results demonstrate that the model based on the first derivative is the best in case of the data referred to the overall spectral range. The correlation coefficients for the training and test sets are respectively equal to 0.938 and 0.965, whereas the root-mean-square errors for these sets amount respectively to 0.286 and 0.577. Our study testifies that the protein content in hazelnuts can be quickly and nondestructively detected using the near-infrared spectroscopy.

Keywords: neural networks, deep learning, partial least-squares method, near-infrared spectroscopy, nondestructive testing, protein content

UDC: 535-1+630*8+004.93

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    Ядра фундука часто можуть бути неповними або деформованими та мати деякі інші дефекти. Ручні методи їхньої класифікації та розпізнавання або навіть застарілі підходи машинного навчання виявляють низьку ефективність розпізнавання та високий рівень неправильних оцінок. У цьому дослідженні ми застосували глибоке навчання до розпізнавання дефектів ядра фундука. Для навчання та розпізнавання використано дві згорткові нейромережеві системи MobileNetV2 і Resnet-50. Виявлено, що точність передбачення ResNet-50 для навчального набору покращена на 10,3%, а коефіцієнт втрат навчання зменшений на 0,041, порівняно з MobileNetV2. Крім того, точність перевірки, досягнута за допомогою ResNet-50, вища на 13,9%, а коефіцієнт втрат перевірки нижчий на 0,151. Це доводить, що загальний тренувальний ефект для нейронної мережі Resnet-50 кращий, ніж для MobileNetV2. За допомогою методу спектроскопії поглинання в близькому інфрачервоному діапазоні нами також вивчено вміст білка в фундуку, який є важливим параметром для оцінки його якості. Для класифікації вибірки використано алгоритм Кеннарда–Стоуна. Щоб розробити методику кількісного аналізу білка в фундуку, використано метод часткових найменших квадратів. Відповідні спектральні дані попередньо оброблено за методами першої похідної, другої похідної та стандартної нормальної змінної. Порівняно вплив цих методів на точність. Результати демонструють, що модель, заснована на першій похідній, є найкращою у разі даних, які стосуються всього спектрального діапазону. Коефіцієнти кореляції для навчальної та тестової вибірок дорівнюють відповідно 0,938 і 0,965, тоді як середньоквадратичні похибки для цих вибірок становлять відповідно 0,286 і 0,577. Наше дослідження засвідчує, що вміст білка в фундуку можна швидко виявити неруйнівним методом за допомогою близької інфрачервоної спектроскопії.

    Ключові слова: нейронні мережі, глибоке навчання, метод часткових найменших квадратів, спектроскопія близького інфрачервоного діапазону, неруйнівний контроль, вміст білка

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