Classification of Bangladeshi soil texture using convolutional neural network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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Brac University
2023
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10361-218112023-10-15T21:05:24Z Classification of Bangladeshi soil texture using convolutional neural network Raj, Hafiz Mohiuddin Shahreen, Sazia Shah, Muntaha Binte Evan, Syed Washinur Ashraf Abdullah, Juhayer Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Soil texture NN models CNN Machine learning Image augmentation Ensemble Soil classification Soils--Classification Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 49-52). In agriculture, soil is one of the most potential output sources. That is why, if we can foresee the soil’s nature and how it will turn in the future as well as it’s other qualities, we may achieve adequate monitoring and sustainable agriculture field usage. We can forecast many soil textures using different CNN models by doing Soil classification. As a result, our major goal is to forecast it and utilize a Convolutional Neural Network (CNN) to do so. We have applied the VGG16, ResNet50, Inception V3, Xception, and VGG19 and these are a kind of algorithm that has the capability to organize a huge number of images of separate divisions. Additionally, in our research, another algorithm is used, which is deeply related to visionary purposes. The algorithms have played a significant role in image augmentation in our research. The input is turned into a set of filters in the hidden layers to construct feature maps in the CNN model. We have used more than 2000 soil images as our data set, which helped for the betterment of our research. Images of several soil samples are used to train and evaluate these models. We have also used more than 4096 soil images of Bangladesh, creating a new scope for our research. A machine vision system consisting of a smartphone camera with an external lens, elimination chamber, USB connection, and a laptop for algorithm processing activities will be used to prepare the data. In general, the current research was carried out with five goals in mind which will be discussed in further depth in the following sections. On photos of different soil samples, these models were trained and tested. With the best accuracy percentage, the suggested models could predict soil pictures. More than 90% of accuracy from each model has been obtained, except for Xception model, where we get an accuracy of 85%. In the end, this approach will be less costly and a waste of time alternative to experimental methods for classifying the kind of soil textures on a broad scale. Hafiz Mohiuddin Raj Sazia Shahreen Muntaha Binte Shah Syed Washinur Ashraf Evan Juhayer Abdullah B.Sc. in Computer Science 2023-10-15T06:43:31Z 2023-10-15T06:43:31Z ©2022 2022-09-29 Thesis ID 18301250 ID 16101247 ID 16241006 ID 18301056 ID 18301251 http://hdl.handle.net/10361/21811 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 65 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Soil texture NN models CNN Machine learning Image augmentation Ensemble Soil classification Soils--Classification Neural networks (Computer science) |
| spellingShingle |
Soil texture NN models CNN Machine learning Image augmentation Ensemble Soil classification Soils--Classification Neural networks (Computer science) Raj, Hafiz Mohiuddin Shahreen, Sazia Shah, Muntaha Binte Evan, Syed Washinur Ashraf Abdullah, Juhayer Classification of Bangladeshi soil texture using convolutional neural network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Raj, Hafiz Mohiuddin Shahreen, Sazia Shah, Muntaha Binte Evan, Syed Washinur Ashraf Abdullah, Juhayer |
| format |
Thesis |
| author |
Raj, Hafiz Mohiuddin Shahreen, Sazia Shah, Muntaha Binte Evan, Syed Washinur Ashraf Abdullah, Juhayer |
| author_sort |
Raj, Hafiz Mohiuddin |
| title |
Classification of Bangladeshi soil texture using convolutional neural network |
| title_short |
Classification of Bangladeshi soil texture using convolutional neural network |
| title_full |
Classification of Bangladeshi soil texture using convolutional neural network |
| title_fullStr |
Classification of Bangladeshi soil texture using convolutional neural network |
| title_full_unstemmed |
Classification of Bangladeshi soil texture using convolutional neural network |
| title_sort |
classification of bangladeshi soil texture using convolutional neural network |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21811 |
| work_keys_str_mv |
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| _version_ |
1814309812388757504 |