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A Novel Convolutional Neural Networks Based Spinach Classification and Recognition System

Sankar Sennan1, Digvijay Pandey2,*, Youseef Alotaibi3, Saleh Alghamdi4

1 Department of Computer Science and Engineering, Sona College of Technology, Salem, 636005, India
2 Department of Technical Education, Department of Electronics Engineering, Institute of Engineering and Technology, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, India
3 Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
4 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: Digvijay Pandey. Email: email

Computers, Materials & Continua 2022, 73(1), 343-361. https://doi.org/10.32604/cmc.2022.028334

Abstract

In the present scenario, Deep Learning (DL) is one of the most popular research algorithms to increase the accuracy of data analysis. Due to intra-class differences and inter-class variation, image classification is one of the most difficult jobs in image processing. Plant or spinach recognition or classification is one of the deep learning applications through its leaf. Spinach is more critical for human skin, bone, and hair, etc. It provides vitamins, iron, minerals, and protein. It is beneficial for diet and is readily available in people's surroundings. Many researchers have proposed various machine learning and deep learning algorithms to classify plant images more accurately in recent years. This paper presents a novel Convolutional Neural Network (CNN) to recognize spinach more accurately. The proposed CNN architecture classifies the spinach category, namely Amaranth leaves, Black nightshade, Curry leaves, and Drumstick leaves. The dataset contains 400 images with four classes, and each type has 100 images. The images were captured from the agricultural land located at Thirumanur, Salem district, Tamil Nadu. The proposed CNN achieves 97.5% classification accuracy. In addition, the performance of the proposed CNN is compared with Support Vector Machine (SVM), Random Forest, Visual Geometry Group 16 (VGG16), Visual Geometry Group 19 (VGG19) and Residual Network 50 (ResNet50). The proposed provides superior performance than other models, namely SVM, Random Forest, VGG16, VGG19 and ResNet50.

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APA Style
Sennan, S., Pandey, D., Alotaibi, Y., Alghamdi, S. (2022). A novel convolutional neural networks based spinach classification and recognition system. Computers, Materials & Continua, 73(1), 343-361. https://doi.org/10.32604/cmc.2022.028334
Vancouver Style
Sennan S, Pandey D, Alotaibi Y, Alghamdi S. A novel convolutional neural networks based spinach classification and recognition system. Comput Mater Contin. 2022;73(1):343-361 https://doi.org/10.32604/cmc.2022.028334
IEEE Style
S. Sennan, D. Pandey, Y. Alotaibi, and S. Alghamdi "A Novel Convolutional Neural Networks Based Spinach Classification and Recognition System," Comput. Mater. Contin., vol. 73, no. 1, pp. 343-361. 2022. https://doi.org/10.32604/cmc.2022.028334



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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