Classification of Soil Images using Convolution Neural Networks

Title

Classification of Soil Images using Convolution Neural Networks

Creator

Chatterjee K.; Obaidat M.S.; Samanta D.; Sadoun B.; Islam S.K.H.; Chatterjee R.

Description

Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. 2021 IEEE.

Source

Proceedings of the 2021 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2021

Date

2021-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Convolution Neural Network; Soil classification; Supervised learning; Transfer learning

Coverage

Chatterjee K., CHRIST (Deemed to Be) University, Department of Computer Science, Karnataka, Bangalore, India; Obaidat M.S., University of Sharjah, College of Computing and Informatics, Sharjah, 27272, United Arab Emirates; Samanta D., CHRIST (Deemed to Be) University, Department of Computer Science, Karnataka, Bangalore, India; Sadoun B., Al-Balqa' Applied University, College of Engineering, Al Salt, Jordan; Islam S.K.H., Indian Institute of Information Technology Kalyani, Department of CSE, West Bengal, 741235, India; Chatterjee R., KIIT Deemed to Be University, School of Computer Engineering, Bhubaneswar, India

Rights

Restricted Access

Relation

ISBN: 978-166543208-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/CCCI52664.2021.9583192

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119292063&doi=10.1109%2fCCCI52664.2021.9583192&partnerID=40&md5=b3c81fb385d018bc22b556be08abcd19