A customized convolutional neural network-based approach for weeds identification in cotton crops
Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases an...
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| Format: | Article |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1435301/full |
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| author | Hafiz Muhammad Faisal Muhammad Aqib Muhammad Aqib Khalid Mahmood Mejdl Safran Sultan Alfarhood Imran Ashraf |
| author_facet | Hafiz Muhammad Faisal Muhammad Aqib Muhammad Aqib Khalid Mahmood Mejdl Safran Sultan Alfarhood Imran Ashraf |
| author_sort | Hafiz Muhammad Faisal |
| collection | DOAJ |
| description | Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production. The cotton crop, is a major cash crop in Asian and African countries and is affected by different types of weeds leading to reduced yield. Weeds infestation starts with the germination of the crop, due to which diseases also invade the field. Therefore, proper monitoring of the cotton crop throughout the entire phases of crop development from sewing to ripening and reaping is extremely significant to identify the harmful and undesired weeds timely and efficiently so that proper measures can be taken to eradicate them. Most of the weeds and pests attack cotton plants at different stages of growth. Therefore, timely identification and classification of such weeds on virtue of their symptoms, apparent similarities, and effects can reduce the risk of yield loss. Weeds and pest infestation can be controlled through advanced digital gadgets like sensors and cameras which can provide a bulk of data to work with. Yet efficient management of this extraordinarily bulging agriculture data is a cardinal challenge for deep learning techniques too. In the given study, an approach based on deep CNN-based architecture is presented. This work covers identifying and classifying the cotton weeds efficiently alongside a comparison of other already existing CNN models like VGG-16, ResNet, DenseNet, and Xception Model. Experimental results indicate the accuracy of VGG-16, ResNet-101, DenseNet-121, XceptionNet as 95.4%, 97.1%, 96.9% and 96.1%, respectively. The proposed model achieved an accuracy of 98.3% outperforming other models. |
| format | Article |
| id | doaj-art-b5587cb2b66b43bf999d3bd034479589 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-b5587cb2b66b43bf999d3bd0344795892025-01-08T04:10:58ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.14353011435301A customized convolutional neural network-based approach for weeds identification in cotton cropsHafiz Muhammad Faisal0Muhammad Aqib1Muhammad Aqib2Khalid Mahmood3Mejdl Safran4Sultan Alfarhood5Imran Ashraf6University Institute of Information Technology (UIIT), Pir Mehr Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, PakistanUniversity Institute of Information Technology (UIIT), Pir Mehr Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, PakistanNational Center of Industrial Biotechnology, Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Punjab, PakistanInstitute of Computing and Information Technology, Gomal University, D.I. Khan, PakistanDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaInformation and Communication Engineering, Yeungnam University, Gyeongsan, Republic of KoreaSmart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production. The cotton crop, is a major cash crop in Asian and African countries and is affected by different types of weeds leading to reduced yield. Weeds infestation starts with the germination of the crop, due to which diseases also invade the field. Therefore, proper monitoring of the cotton crop throughout the entire phases of crop development from sewing to ripening and reaping is extremely significant to identify the harmful and undesired weeds timely and efficiently so that proper measures can be taken to eradicate them. Most of the weeds and pests attack cotton plants at different stages of growth. Therefore, timely identification and classification of such weeds on virtue of their symptoms, apparent similarities, and effects can reduce the risk of yield loss. Weeds and pest infestation can be controlled through advanced digital gadgets like sensors and cameras which can provide a bulk of data to work with. Yet efficient management of this extraordinarily bulging agriculture data is a cardinal challenge for deep learning techniques too. In the given study, an approach based on deep CNN-based architecture is presented. This work covers identifying and classifying the cotton weeds efficiently alongside a comparison of other already existing CNN models like VGG-16, ResNet, DenseNet, and Xception Model. Experimental results indicate the accuracy of VGG-16, ResNet-101, DenseNet-121, XceptionNet as 95.4%, 97.1%, 96.9% and 96.1%, respectively. The proposed model achieved an accuracy of 98.3% outperforming other models.https://www.frontiersin.org/articles/10.3389/fpls.2024.1435301/fulldeep learningconvolutional neural networksobject classificationcotton crops weedsweeds detection |
| spellingShingle | Hafiz Muhammad Faisal Muhammad Aqib Muhammad Aqib Khalid Mahmood Mejdl Safran Sultan Alfarhood Imran Ashraf A customized convolutional neural network-based approach for weeds identification in cotton crops Frontiers in Plant Science deep learning convolutional neural networks object classification cotton crops weeds weeds detection |
| title | A customized convolutional neural network-based approach for weeds identification in cotton crops |
| title_full | A customized convolutional neural network-based approach for weeds identification in cotton crops |
| title_fullStr | A customized convolutional neural network-based approach for weeds identification in cotton crops |
| title_full_unstemmed | A customized convolutional neural network-based approach for weeds identification in cotton crops |
| title_short | A customized convolutional neural network-based approach for weeds identification in cotton crops |
| title_sort | customized convolutional neural network based approach for weeds identification in cotton crops |
| topic | deep learning convolutional neural networks object classification cotton crops weeds weeds detection |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1435301/full |
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