Optimization of crop disease classification using convolution neural network
This paper presents the deep learning model by Convolution Neural Network (CNN) in training the crop disease classifier via image classification. A camera will be equipped and applied in artificial intelligent drone to operate as a crop monitoring system used for agriculture. Agriculture productivit...
| Main Authors: | , , , , , |
|---|---|
| Format: | Proceedings |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers
2021
|
| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/32510/1/Optimization%20of%20crop%20disease%20classification%20using%20convolution%20neural%20network.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/32510/2/Optimization%20of%20Crop%20Disease%20Classification%20using%20Convolution%20Neural%20Network.pdf |
| _version_ | 1796911231597019136 |
|---|---|
| author | Kit, Guan Lim Chii, Soon Huong Min, Keng Tan Chung, Fan Liau Min, Yang Tze, Kenneth Kin Teo |
| author_facet | Kit, Guan Lim Chii, Soon Huong Min, Keng Tan Chung, Fan Liau Min, Yang Tze, Kenneth Kin Teo |
| author_sort | Kit, Guan Lim |
| collection | UMS |
| description | This paper presents the deep learning model by Convolution Neural Network (CNN) in training the crop disease classifier via image classification. A camera will be equipped and applied in artificial intelligent drone to operate as a crop monitoring system used for agriculture. Agriculture productivity is a key component of country economy. Crop diseases can lead to a drop in the quality and quantity of agricultural products. Famers are facing problems to detect the crop diseases accurately in huge region of crops. Therefore, CNN based method for crop disease detection is proposed. Dataset contains of 16,257 color images which has a total of categories have been fed into the model, out of which 10 categories are of diseased crop leaves. The CNN model contains 7 convolution layers with the number of filters 32, 64, two layers with 128 filters, three layers with 256 filters and filter size $3\times 3$ is the proposed approach to perform crop disease classification, with the best testing accuracy of 99.02%. The crops are classified correctly using the suggested CNN design. The suggested CNN design is validated and evaluated which achieves accuracy of 99.02%, 0.98% error, 99% recall, 99% precision and 0.99 score of F-measure. In this paper, achievement of the proposed CNN model is reaching a promising result and simulated successfully in classifying the crop disease. |
| first_indexed | 2024-03-06T03:15:48Z |
| format | Proceedings |
| id | ums.eprints-32510 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:15:48Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | ums.eprints-325102022-05-19T02:21:08Z https://eprints.ums.edu.my/id/eprint/32510/ Optimization of crop disease classification using convolution neural network Kit, Guan Lim Chii, Soon Huong Min, Keng Tan Chung, Fan Liau Min, Yang Tze, Kenneth Kin Teo QA75.5-76.95 Electronic computers. Computer science SB1-1110 Plant culture This paper presents the deep learning model by Convolution Neural Network (CNN) in training the crop disease classifier via image classification. A camera will be equipped and applied in artificial intelligent drone to operate as a crop monitoring system used for agriculture. Agriculture productivity is a key component of country economy. Crop diseases can lead to a drop in the quality and quantity of agricultural products. Famers are facing problems to detect the crop diseases accurately in huge region of crops. Therefore, CNN based method for crop disease detection is proposed. Dataset contains of 16,257 color images which has a total of categories have been fed into the model, out of which 10 categories are of diseased crop leaves. The CNN model contains 7 convolution layers with the number of filters 32, 64, two layers with 128 filters, three layers with 256 filters and filter size $3\times 3$ is the proposed approach to perform crop disease classification, with the best testing accuracy of 99.02%. The crops are classified correctly using the suggested CNN design. The suggested CNN design is validated and evaluated which achieves accuracy of 99.02%, 0.98% error, 99% recall, 99% precision and 0.99 score of F-measure. In this paper, achievement of the proposed CNN model is reaching a promising result and simulated successfully in classifying the crop disease. Institute of Electrical and Electronics Engineers 2021-09-29 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32510/1/Optimization%20of%20crop%20disease%20classification%20using%20convolution%20neural%20network.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/32510/2/Optimization%20of%20Crop%20Disease%20Classification%20using%20Convolution%20Neural%20Network.pdf Kit, Guan Lim and Chii, Soon Huong and Min, Keng Tan and Chung, Fan Liau and Min, Yang and Tze, Kenneth Kin Teo (2021) Optimization of crop disease classification using convolution neural network. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science SB1-1110 Plant culture Kit, Guan Lim Chii, Soon Huong Min, Keng Tan Chung, Fan Liau Min, Yang Tze, Kenneth Kin Teo Optimization of crop disease classification using convolution neural network |
| title | Optimization of crop disease classification using convolution neural network |
| title_full | Optimization of crop disease classification using convolution neural network |
| title_fullStr | Optimization of crop disease classification using convolution neural network |
| title_full_unstemmed | Optimization of crop disease classification using convolution neural network |
| title_short | Optimization of crop disease classification using convolution neural network |
| title_sort | optimization of crop disease classification using convolution neural network |
| topic | QA75.5-76.95 Electronic computers. Computer science SB1-1110 Plant culture |
| url | https://eprints.ums.edu.my/id/eprint/32510/1/Optimization%20of%20crop%20disease%20classification%20using%20convolution%20neural%20network.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/32510/2/Optimization%20of%20Crop%20Disease%20Classification%20using%20Convolution%20Neural%20Network.pdf |
| work_keys_str_mv | AT kitguanlim optimizationofcropdiseaseclassificationusingconvolutionneuralnetwork AT chiisoonhuong optimizationofcropdiseaseclassificationusingconvolutionneuralnetwork AT minkengtan optimizationofcropdiseaseclassificationusingconvolutionneuralnetwork AT chungfanliau optimizationofcropdiseaseclassificationusingconvolutionneuralnetwork AT minyang optimizationofcropdiseaseclassificationusingconvolutionneuralnetwork AT tzekennethkinteo optimizationofcropdiseaseclassificationusingconvolutionneuralnetwork |