Adaptive Deep Learning with Optimization Hybrid Convolutional Neural Network and Recurrent Neural Network for Prediction Lemon Fruit Ripeness

被引：0

作者：

Watnakornbuncha, Darunee ^{[1
]}

Am-Dee, Noppadol ^{[1
]}

Sangsongfa, Adisak ^{[1
]}

机构：

[1] Muban Chom Bueng Rajabhat Univ, Doctoral Program Ind Technol Management, Ratchaburi, Thailand

来源：

PRZEGLAD ELEKTROTECHNICZNY | 2024年 / 100卷 / 03期

关键词：

Lemon; Convolutional Neural Network; Recurrent Neural Network;

D O I：

10.15199/48.2024.03.36

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Lemon is a valuable fruit in the citrus family; optimal usage requires careful selection. The study categorized lemon suitability prediction int 4 classes based on image data. A hybrid neural network, combining Convolutional and Recurrent Neural Networks, was optimized with the Particle Swarm Optimization algorithm. Experimental results were compared to using Convolutional Neural Network alone. The prediction yielded 89.83% training accuracy and 66.06% testing accuracy. However, combining the results increased training accuracy to 91.58% and testing accuracy to 86.76%.

引用

页码：202 / 211

页数：10

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