Classification of hazelnuts according to their quality using deep learning algorithms

Erbaş, Nizamettin; Çınarer, Gökalp; Kiliç, Kazım

doi:10.17221/21/2022-cjfs

Cited by 8 publications

(4 citation statements)

References 19 publications

(17 reference statements)

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“…For example, logistic regression can be used to predict whether a patient has a disease or whether a customer will buy a product. Logistic regression uses a sigmoid function to estimate the effects of independent variables [14]. This function expresses the probability that a sample belongs to a class, as a value between 0 and 1.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Feyzioğlu

Taşpınar

2023

International Journal of Applied Mathematics Electronics and Computers

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Iron metal is the most widely used metal type. This metal, which is used in countless sectors, is processed in different ways and turned into steel. Since steel has a brittle structure compared to iron, defects may occur in the plates during the rolling process. Detection of these defects at the production stage is of great importance in terms of commercial and safety. Machine learning methods can be used in such problems for fast and high accuracy detection. For this purpose, using a dataset obtained from stainless steel surface defects in this study, classification processes were carried out to detect defects with four different machine learning methods. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for classification processes. The highest classification accuracy was obtained from the 79.44% RF model. Correlation analysis was performed in order to analyze the effects of the features in the dataset on the classification results. It is thought that the classification accuracy of the proposed models is satisfactory for this challenging problem, but needs to be upgraded.

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Section: Logistic Regression (Lr)mentioning

confidence: 99%

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Feyzioğlu

Taşpınar

2023

International Journal of Applied Mathematics Electronics and Computers

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“…AI technologies, which adapt quickly to agricultural processes, especially in developed countries, apply by most producers. AI applications provide with a chance for more profitable and productive agriculture by detecting the problems in agricultural production and marketing [9].…”

Section: Introductionmentioning

confidence: 99%

Rice Classification and Quality Detection Success with Artificial Intelligence Technologies

Çınarer,

Erbaş,

Öcal

2024

Braz. arch. biol. technol.

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Rice is the most consumed and the most traded food in the world, and so it is very important for it to be classified correctly by its qualities. In this study, the success situation in the classification of rice by qualities with information technologies systems was aimed. In the study, the feature selection process was applied by making statistical analyzes of the features obtained from the images of two different rice species. The classification process was carried out with five different Artificial Intelligence (AI) algorithms using 6 different morphological features. When the results and performance values are examined, it was viewed that the Support Vector Machine (SVM) algorithm gave the highest accuracy in classification with 93.53%. The obtained Area Under the Curve (AUC) values showed that a very high classification result of 99.18% was accomplished. It was detected that morphological features were very important parameters in classifying rice varieties with the AI algorithms. It is accepted that this study will be important in accelerating the process of product classification which is one of the main components of agricultural marketing and classifying correctly crops.

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“…Consumers are no longer satisfied with the bare minimum. Their heightened awareness and evolving preferences demand a relentless pursuit of excellence [2]. Despite advancements in technology, a significant portion of food quality evaluation remains stubbornly manual.…”

Section: Introductionmentioning

confidence: 99%

Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-Based Model

Asghari Ilani,

Moftakhar Tehran,

Kavei

et al. 2024

Preprint

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In response to the burgeoning global demand for premium agricultural products, particularly within the competitive nut market, this paper introduces an innovative methodology aimed at enhancing the grading process for almonds and their shells. Leveraging state-of-the-art Deep Convolutional Neural Networks (CNNs), specifically the AlmondNet-20 architecture, our study achieves exceptional accuracy exceeding 99%, facilitated by the utilization of a 20-layer CNN model. To bolster robustness in differentiating between almonds and shells, data augmentation techniques are employed, ensuring the reliability and accuracy of our classification system. Our model, meticulously trained over 1000 epochs, demonstrates remarkable performance, boasting an accuracy rate of 99% alongside a minimal loss function of 0.0567. Rigorous evaluation through test datasets further validates the efficacy of our approach, revealing impeccable precision, recall, and F1-score metrics for almond detection. Beyond its technical prowess, this advanced classification system offers tangible benefits to both industry experts and non-specialists alike, ensuring globally reliable almond classification. The application of deep learning algorithms, as showcased in our study, not only enhances grading accuracy but also presents opportunities for product patents, thereby contributing to the economic value of our nation. Through the adoption of cutting-edge technologies such as the AlmondNet-20 model, we pave the way for future advancements in agricultural product classification, ultimately enriching global trade and economic prosperity.

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Classification of hazelnuts according to their quality using deep learning algorithms

Cited by 8 publications

References 19 publications

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Rice Classification and Quality Detection Success with Artificial Intelligence Technologies

Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-Based Model

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