A Parallel Convolution and Decision Fusion-Based Flower Classification Method

Jia, Lianyin; Zhai, Hongsong; Yuan, Xiaohui; Jiang, Ying; Ding, Jiang

doi:10.3390/math10152767

Cited by 4 publications

(2 citation statements)

References 45 publications

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“…Visual Geometry Group 16 (VGG16), developed by Oxford University’s Visual Geometry Group in 2014, is a convolutional neural network adept at processing image data [ 44 ]. VGG16 consists of five convolutional blocks and three fully connected layers.…”

Section: Methodsmentioning

confidence: 99%

Development of Multimodal Fusion Technology for Tomato Maturity Assessment

Liu,

Wei,

Yoon

et al. 2024

Sensors

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The maturity of fruits and vegetables such as tomatoes significantly impacts indicators of their quality, such as taste, nutritional value, and shelf life, making maturity determination vital in agricultural production and the food processing industry. Tomatoes mature from the inside out, leading to an uneven ripening process inside and outside, and these situations make it very challenging to judge their maturity with the help of a single modality. In this paper, we propose a deep learning-assisted multimodal data fusion technique combining color imaging, spectroscopy, and haptic sensing for the maturity assessment of tomatoes. The method uses feature fusion to integrate feature information from images, near-infrared spectra, and haptic modalities into a unified feature set and then classifies the maturity of tomatoes through deep learning. Each modality independently extracts features, capturing the tomatoes’ exterior color from color images, internal and surface spectral features linked to chemical compositions in the visible and near-infrared spectra (350 nm to 1100 nm), and physical firmness using haptic sensing. By combining preprocessed and extracted features from multiple modalities, data fusion creates a comprehensive representation of information from all three modalities using an eigenvector in an eigenspace suitable for tomato maturity assessment. Then, a fully connected neural network is constructed to process these fused data. This neural network model achieves 99.4% accuracy in tomato maturity classification, surpassing single-modal methods (color imaging: 94.2%; spectroscopy: 87.8%; haptics: 87.2%). For internal and external maturity unevenness, the classification accuracy reaches 94.4%, demonstrating effective results. A comparative analysis of performance between multimodal fusion and single-modal methods validates the stability and applicability of the multimodal fusion technique. These findings demonstrate the key benefits of multimodal fusion in terms of improving the accuracy of tomato ripening classification and provide a strong theoretical and practical basis for applying multimodal fusion technology to classify the quality and maturity of other fruits and vegetables. Utilizing deep learning (a fully connected neural network) for processing multimodal data provides a new and efficient non-destructive approach for the massive classification of agricultural and food products.

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Section: Methodsmentioning

confidence: 99%

Development of Multimodal Fusion Technology for Tomato Maturity Assessment

Liu,

Wei,

Yoon

et al. 2024

Sensors

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show abstract

“…Flowers are plants that often grow around [1]. That estimated there are at least 369,000 types of flowers that scattered in the world, so with many species that scattered, there are several species flower that have similar characteristics to each other, so making it difficult to identify based on differences in existing flowers in terms of shape, color or texture of the flowers [2,3,4,5]. Roses are a type of flower that is much loved because it has its own fragrance and beauty, so widely used as an ornamental plant, fragrance, and as a raw material for making food or drinks [6].…”

Section: Introductionmentioning

confidence: 99%

A Comparative study of Transfer Learning CNN for Flower Type Classification

Sumpena

2023

JAIS

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Flowers are plants that had many types and often found around. But because the many types of flowers, sometimes difficult to distinguish the type from one flower to another. Therefore, in this study, will discuse about the process of identification and classification of flower types, namely daisy, dandelion, rose, sunflower and tulip. The data that would used in this research is image data that consisting of 764 daisy images, 1052 dandelion images, 784 rose images, 733 sunflower images and 984 tulip images. From the total images used, would be divided again into 60% training data, 30% testing data and 10% validation data that would been used to train and evaluate the CNN model. In this study, the classification process would using transfer learning CNN method using the DenseNet and NasNetLarge architectures, which later from these two architectures would compare to find which architecture is best for classifying flower types. The results that obtained after testing in this study are in the flower classification process using the DenseNet architecture to get a test accuracy of 89% and using the NasLargeNet architecture to get a test accuracy of 86%.

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UAV remote sensing image stitching via improved VGG16 Siamese feature extraction network

Zhu

et al. 2023

Expert Systems with Applications

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A Parallel Convolution and Decision Fusion-Based Flower Classification Method

Cited by 4 publications

References 45 publications

Development of Multimodal Fusion Technology for Tomato Maturity Assessment

Development of Multimodal Fusion Technology for Tomato Maturity Assessment

A Comparative study of Transfer Learning CNN for Flower Type Classification

UAV remote sensing image stitching via improved VGG16 Siamese feature extraction network

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