Classification of hazelnut kernels with deep learning

Ünal, Zeynep; Aktaş, Hakan

doi:10.1016/j.postharvbio.2022.112225

Cited by 18 publications

(6 citation statements)

References 24 publications

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“…Deep Learning (DL), a subset of machine learning, has profoundly transformed various domains, ushering in a paradigm shift in academia, healthcare, finance, and agriculture [7,8]. In education, DL has revolutionized pedagogical strategies through personalized learning platforms, adaptive assessment systems, and intelligent tutoring systems, tailoring education to individual needs [9].…”

Section: Deep Learningmentioning

confidence: 99%

Adaptability of deep learning: datasets and strategies in fruit classification

Gulzar,

Ünal,

Ayoub

et al. 2024

BIO Web Conf.

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This review aims to uncover the multifaceted landscape of methodologies employed by researchers for accurate fruit classification. The exploration encompasses an array of techniques and models, each tailored to address the nuanced challenges presented by fruit classification tasks. From convolutional neural networks (CNNs) to recurrent neural networks (RNNs), and transfer learning to ensemble methods, the spectrum of approaches underscores the innovative strategies harnessed to achieve precision in fruit categorization. A significant facet of this review lies in the analysis of the various datasets utilized by researchers for fruit classification. Different datasets present unique challenges and opportunities, thereby shaping the design and effectiveness of the models. From widely recognized datasets like Fruits-360 to specialized collections, the review navigates through a plethora of data sources, elucidating how these datasets contribute to the diversity of research endeavors. This insight not only highlights the variety in fruit types and attributes but also emphasizes the adaptability of deep learning techniques to accommodate these variations. By amalgamating findings from diverse articles, this study offers an enriched understanding of the evolving trends and advancements within the domain of fruit classification using deep learning. The synthesis of methodologies and dataset variations serves to inform future research pursuits, aiding in the refinement of accurate and robust fruit classification methods. As the field progresses, this review stands as a valuable compass, guiding researchers toward impactful contributions that enhance the accuracy and applicability of fruit classification models.

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Section: Deep Learningmentioning

confidence: 99%

Adaptability of deep learning: datasets and strategies in fruit classification

Gulzar,

Ünal,

Ayoub

et al. 2024

BIO Web Conf.

Self Cite

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“…Based on these images, the products from the hazelnut cracking machine were classified into undersized, damaged kernel, whole kernel, and shell. In the classification stage, they achieved classification accuracies of 97.85% and 99.28% using pre-trained transfer learning models, InceptionV3 and EfficientNet, respectively [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Hazelnut Species with Pre-Trained Deep Learning Models

Harmancı,

Ünal,

Ateş

2023

imiens

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A form of shelled nut in the Betulaceae family is the hazelnut. The majority of it is grown in Türkiye internationally. It grows in the provinces of Türkiye's Black Sea region, which is a significant global production hub. Hazelnuts can be eaten in a variety of ways and are a great source of protein, fat, fiber, vitamins, and minerals. There are numerous applications for hazelnuts in the food business. This study uses pre-trained networks to categorize eight of the most popular hazelnut kinds farmed in Türkiye. In this study, locally named hazelnut varieties grown in Türkiye were examined. An automated computer vision system was used to capture the images of the different hazelnut kinds. Our dataset includes a total of 2722 images, consisting of 155 palaz, 340 yagli, 399 deve disi, 236 tombul, 399 damat, 354 cakildak, 437 kara findik, and 402 sivri hazelnuts. Using transfer learning, the DenseNet121 and InceptionV3 models of convolutional neural networks were employed to categorize these images. The dataset was split into training and testing portions, respectively. With InceptionV3 and DenseNet121, respectively, the research revealed classification accuracy of 96.99% and 96.18%.

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“…The modified proposed model has achieved 99% accuracy and has outperformed state-of-art models such as VGG16, AlexNet, and other models. whereas in another study Aktas et al [31,32] trained AlexNet and InceptionV2 models to classify open and closed pistachios and achieved 96.13% and 96.54%, respectively. In another study, Dowod et al [33] proposed a model to classify foliar diseases in sunflowers based on ResNet architecture.…”

Section: Introductionmentioning

confidence: 99%

Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study

et al. 2023

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Sunflower is an important crop that is susceptible to various diseases, which can significantly impact crop yield and quality. Early and accurate detection of these diseases is crucial for implementing appropriate management strategies. In recent years, deep learning techniques have shown promising results in the field of disease classification using image data. This study presents a comparative analysis of different deep-learning models for the classification of sunflower diseases. five widely used deep learning models, namely AlexNet, VGG16, InceptionV3, MobileNetV3, and EfficientNet were trained and evaluated using a dataset of sunflower disease images. The performance of each model was measured in terms of precision, recall, F1-score, and accuracy. The experimental results demonstrated that all the deep learning models achieved high precision, recall, F1-score, and accuracy values for sunflower disease classification. Among the models, EfficientNetB3 exhibited the highest precision, recall, F1-score, and accuracy of 0.979. whereas the other models, ALexNet, VGG16, InceptionV3 and MobileNetV3 achieved 0.865, 0.965, 0.954 and 0.969 accuracy respectively. Based on the comparative analysis, it can be concluded that deep learning models are effective for the classification of sunflower diseases. The results highlight the potential of deep learning in early disease detection and classification, which can assist farmers and agronomists in implementing timely disease management strategies. Furthermore, the findings suggest that models like MobileNetV3 and EfficientNetB3 could be preferred choices due to their high performance and relatively fewer training epochs.

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Classification of hazelnut kernels with deep learning

Cited by 18 publications

References 24 publications

Adaptability of deep learning: datasets and strategies in fruit classification

Adaptability of deep learning: datasets and strategies in fruit classification

Classification of Hazelnut Species with Pre-Trained Deep Learning Models

Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study

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