Harnessing artificial intelligence for advancements in Rice / wheat functional food Research and Development

Food Research International, Год журнала: 2025, Номер unknown, С. 116306 - 116306

Опубликована: Март 1, 2025

Язык: Английский

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Classification of bread wheat genotypes by machine learning algorithms

Journal of Food Composition and Analysis, Год журнала: 2023, Номер 119, С. 105253 - 105253

Опубликована: Фев. 28, 2023

Язык: Английский

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Analysis of selected deep features with CNN-SVM-based for bread wheat seed classification

European Food Research and Technology, Год журнала: 2024, Номер 250(6), С. 1551 - 1561

Опубликована: Март 14, 2024

Abstract The main ingredient of flour is processed wheat. Wheat an agricultural product that harvested once a year. It may be necessary to choose the variety wheat for growing and efficient harvesting. important its economic value, taste, crop yield. Although there are many varieties wheat, they very similar in colour, size, shape, it requires expertise distinguish them by eye. This time consuming can lead human error. Using computer vision artificial intelligence, such problems solved more quickly objectively. In this study, attempt was made classify five bread belonging different cultivars using Convolutional Neural Network (CNN) models. Three approaches have been proposed classification. First, pre-trained CNN models (ResNet18, ResNet50, ResNet101) were trained cultivars. Second, features extracted from fc1000 layer ResNet18, ResNet101 classified support vector machine (SVM) classifier with kernel learning techniques classification variants. Finally, SVM methods used second stage obtained optimal set represent all minimum redundancy maximum relevance (mRMR) feature selection algorithm.The accuracies first, second, last phases as follows. first phase, most successful method classifying grains ResNet18 model 97.57%. + ResNet50 Quadratic ResNet 94.08%.The accuracy 1000 effective selected algorithm 94.51%. slightly lower than deep learning, much shorter 93%. result confirms great effectiveness grain

Язык: Английский

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A comparative study of bread wheat varieties identification on feature extraction, feature selection and machine learning algorithms

European Food Research and Technology, Год журнала: 2023, Номер 250(1), С. 135 - 149

Опубликована: Окт. 1, 2023

Язык: Английский

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Classification of bread wheat varieties with a combination of deep learning approach

European Food Research and Technology, Год журнала: 2023, Номер 250(1), С. 181 - 189

Опубликована: Окт. 13, 2023

Язык: Английский

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Maize seeds forecasting with hybrid directional and bi‐directional long short‐term memory models

Food Science & Nutrition, Год журнала: 2023, Номер 12(2), С. 786 - 803

Опубликована: Ноя. 9, 2023

The purity of the seeds is one important factors that increase yield. For this reason, classification maize cultivars constitutes a significant problem. Within scope study, six different models were designed to solve A special dataset was created be used in for study. contains total 14,469 images four classes. Images belong types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from BIOTEK company. AlexNet ResNet50 architectures, with transfer learning method, image classification. In order improve success, LSTM (Directional Long Short-Term Memory) BiLSTM (Bi-directional algorithms architectures hybridized. As result classifications, highest success obtained ResNet50+BiLSTM model 98.10%.

Язык: Английский

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Automatic Grading of Barley Grain for Brewery Industries using Convolutional Neural Network Based on Texture Features

Journal of Agriculture and Food Research, Год журнала: 2025, Номер unknown, С. 101752 - 101752

Опубликована: Фев. 1, 2025

Язык: Английский

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Deep learning and evolutionary intelligence with fusion-based feature extraction for classification of wheat varieties

European Food Research and Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 30, 2025

Язык: Английский

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Classification of Medicinal Plant Leaves for Types and Diseases with Hybrid Deep Learning Methods

Information Technology And Control, Год журнала: 2024, Номер 53(1), С. 19 - 36

Опубликована: Март 22, 2024

Leaf images are often used to detect plant diseases because most disease symptoms appear on the leaves. Analyzes performed by experts in laboratory environment expensive and time consuming. Therefore, there is a need for automated detection systems that both economical can help diagnose early more accurately. In this study, deep learning-based methodology presented classification of leaf plants, which very similar color, texture, vein shape cannot be noticed non-experts, important traditional medicine pharmaceutical industry. model development process, 7 pre-learning learning algorithms an image data set created from leaves 10 categories were preferred. The proposed classifies type diseased condition dataset. first step training model, different rates tested with optimum hyperparameters. second part, test accuracy rate 98.69% was achieved DenseNet121 increased data. At last stage, after edge processes, value 67.92% reached DenseNet 121 model.

Язык: Английский

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Revolutionizing lemon grading: an automated CNN-based approach for enhanced quality assessment

International Journal of Information Technology, Год журнала: 2024, Номер 16(7), С. 4155 - 4166

Опубликована: Май 2, 2024

Язык: Английский

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