Open Access

ARTICLE

A Novel Convolutional Neural Networks Based Spinach Classification and Recognition System

Sankar Sennan¹, Digvijay Pandey^2,*, 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:

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

Received 07 February 2022; Accepted 31 March 2022; Issue published 18 May 2022

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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Keywords

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