The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms
Weeds are a major threat to crops, making early detection critical for maintaining agricultural productivity. Weeds are generally toxic, equipped with thorns and burrs, and can disrupt crop management by contaminating harvests. This research aims to identify weeds in a field using image processing a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01057.pdf |
| _version_ | 1797807812077682688 |
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| author | Subbarayudu Yerragudipadu Soppadandi Adithi Vyamasani Shreya Bandanadam Supriya |
| author_facet | Subbarayudu Yerragudipadu Soppadandi Adithi Vyamasani Shreya Bandanadam Supriya |
| author_sort | Subbarayudu Yerragudipadu |
| collection | DOAJ |
| description | Weeds are a major threat to crops, making early detection critical for maintaining agricultural productivity. Weeds are generally toxic, equipped with thorns and burrs, and can disrupt crop management by contaminating harvests. This research aims to identify weeds in a field using image processing and deep learning techniques. Images were collected from an Indian farm and pre-processed using image processing techniques. The images were then analysed to extract features that distinguish between weed and crop properties. Traditional crop weed identification methods mainly focused on identifying weeds directly but weed species can vary significantly. This study proposes a method that combines deep learning and image processing technology. Identifying weeds in crops is a challenging task that has been addressed through image processing, feature extraction, and image labelling to train deep learning algorithms. The study examines the performance of various deep learning algorithms and convolution neural networks to detect weeds using images obtained from an Indian crop field. Once the input image is identified as a weed or not, the crop class prediction is made. These results could have significant implications for optimizing agricultural fertilizer usage, leading to increased crop yields and less environmental impact. |
| first_indexed | 2024-03-13T06:28:14Z |
| format | Article |
| id | doaj.art-c5b08a071db34862a8e58c192b336ba2 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:28:14Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-c5b08a071db34862a8e58c192b336ba22023-06-09T09:12:17ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013910105710.1051/e3sconf/202339101057e3sconf_icmed-icmpc2023_01057The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural FarmsSubbarayudu Yerragudipadu0Soppadandi Adithi1Vyamasani Shreya2Bandanadam Supriya3Department of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETWeeds are a major threat to crops, making early detection critical for maintaining agricultural productivity. Weeds are generally toxic, equipped with thorns and burrs, and can disrupt crop management by contaminating harvests. This research aims to identify weeds in a field using image processing and deep learning techniques. Images were collected from an Indian farm and pre-processed using image processing techniques. The images were then analysed to extract features that distinguish between weed and crop properties. Traditional crop weed identification methods mainly focused on identifying weeds directly but weed species can vary significantly. This study proposes a method that combines deep learning and image processing technology. Identifying weeds in crops is a challenging task that has been addressed through image processing, feature extraction, and image labelling to train deep learning algorithms. The study examines the performance of various deep learning algorithms and convolution neural networks to detect weeds using images obtained from an Indian crop field. Once the input image is identified as a weed or not, the crop class prediction is made. These results could have significant implications for optimizing agricultural fertilizer usage, leading to increased crop yields and less environmental impact.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01057.pdf |
| spellingShingle | Subbarayudu Yerragudipadu Soppadandi Adithi Vyamasani Shreya Bandanadam Supriya The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms E3S Web of Conferences |
| title | The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms |
| title_full | The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms |
| title_fullStr | The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms |
| title_full_unstemmed | The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms |
| title_short | The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms |
| title_sort | distributed deep learning paradigms for detection of weeds from crops in indian agricultural farms |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01057.pdf |
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