Fuzzy rank fusion of deep neural networks for weed identification in groundnut crop
Abstract Herbicides are the primary method for weed control in modern agriculture. Excessive herbicide usage harms both the soil and the environment. Large-scale weed identification by hand is impractical due to its expense and labour intensity, leading to suboptimal results. By combining selective...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Sustainability |
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| Online Access: | https://doi.org/10.1007/s43621-025-01705-9 |
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| author | Akshay Dheeraj Rajnish Kumar Chaturvedi Sapna Nigam Md. Ashraful Haque Sudeep Marwaha |
| author_facet | Akshay Dheeraj Rajnish Kumar Chaturvedi Sapna Nigam Md. Ashraful Haque Sudeep Marwaha |
| author_sort | Akshay Dheeraj |
| collection | DOAJ |
| description | Abstract Herbicides are the primary method for weed control in modern agriculture. Excessive herbicide usage harms both the soil and the environment. Large-scale weed identification by hand is impractical due to its expense and labour intensity, leading to suboptimal results. By combining selective and variable rate spraying, the smart herbicide sprayer effectively manages herbicide amounts. The accurate identification of weed type and density is crucial for its efficiency. In this study, we propose the ensemble of the lightweight deep neural networks, namely EfficientNetV2B0 and DenseNet121 for weed identification in the groundnut crop. These models are pretrained on ImageNet dataset. By using fuzzy rank-based fusion and three non-linear functions (tanH, exp and softplus), the proposed ensemble scheme combines the probabilistic scores of two base models. The final predictions on test samples are determined by the proposed ensemble technique, which differs from existing fusion schemes by incorporating the confidence in base model predictions. The proposed ensemble model has been trained and tested on the publicly available groundnut weed dataset (24,816 images of 16 weed classes), with tenfold cross validation technique for better performance results. Base models EfficientNetV2B0 and DenseNet121 have achieved the classification accuracy of 98.66 ± 0.39 and 98.92 ± 0.19 respectively. Furthermore, with the fuzzy rank-based fusion of these base models obtains an accuracy of 99.10 ± 0.18 with tenfold cross validation, thus increasing the overall performance and outperforms various state of the art models. The proposed fusion model of lightweight CNNs, can be embedded in IoT based agricultural system for weed identification in groundnut crop. This research contributes to sustainable agriculture through targeted herbicide application and reduced chemical overuse. |
| format | Article |
| id | doaj-art-b21607255d8947e8bd43ad703eb7bd70 |
| institution | Kabale University |
| issn | 2662-9984 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Sustainability |
| spelling | doaj-art-b21607255d8947e8bd43ad703eb7bd702025-08-20T04:01:42ZengSpringerDiscover Sustainability2662-99842025-08-016112710.1007/s43621-025-01705-9Fuzzy rank fusion of deep neural networks for weed identification in groundnut cropAkshay Dheeraj0Rajnish Kumar Chaturvedi1Sapna Nigam2Md. Ashraful Haque3Sudeep Marwaha4ICAR-Indian Agricultural Statistics Research InstituteBennett UniversityICAR-Indian Agricultural Statistics Research InstituteICAR-Indian Agricultural Statistics Research InstituteICAR-Indian Agricultural Statistics Research InstituteAbstract Herbicides are the primary method for weed control in modern agriculture. Excessive herbicide usage harms both the soil and the environment. Large-scale weed identification by hand is impractical due to its expense and labour intensity, leading to suboptimal results. By combining selective and variable rate spraying, the smart herbicide sprayer effectively manages herbicide amounts. The accurate identification of weed type and density is crucial for its efficiency. In this study, we propose the ensemble of the lightweight deep neural networks, namely EfficientNetV2B0 and DenseNet121 for weed identification in the groundnut crop. These models are pretrained on ImageNet dataset. By using fuzzy rank-based fusion and three non-linear functions (tanH, exp and softplus), the proposed ensemble scheme combines the probabilistic scores of two base models. The final predictions on test samples are determined by the proposed ensemble technique, which differs from existing fusion schemes by incorporating the confidence in base model predictions. The proposed ensemble model has been trained and tested on the publicly available groundnut weed dataset (24,816 images of 16 weed classes), with tenfold cross validation technique for better performance results. Base models EfficientNetV2B0 and DenseNet121 have achieved the classification accuracy of 98.66 ± 0.39 and 98.92 ± 0.19 respectively. Furthermore, with the fuzzy rank-based fusion of these base models obtains an accuracy of 99.10 ± 0.18 with tenfold cross validation, thus increasing the overall performance and outperforms various state of the art models. The proposed fusion model of lightweight CNNs, can be embedded in IoT based agricultural system for weed identification in groundnut crop. This research contributes to sustainable agriculture through targeted herbicide application and reduced chemical overuse.https://doi.org/10.1007/s43621-025-01705-9Deep learningEnsemble modelFuzzy logicSmart agricultureWeed classification |
| spellingShingle | Akshay Dheeraj Rajnish Kumar Chaturvedi Sapna Nigam Md. Ashraful Haque Sudeep Marwaha Fuzzy rank fusion of deep neural networks for weed identification in groundnut crop Discover Sustainability Deep learning Ensemble model Fuzzy logic Smart agriculture Weed classification |
| title | Fuzzy rank fusion of deep neural networks for weed identification in groundnut crop |
| title_full | Fuzzy rank fusion of deep neural networks for weed identification in groundnut crop |
| title_fullStr | Fuzzy rank fusion of deep neural networks for weed identification in groundnut crop |
| title_full_unstemmed | Fuzzy rank fusion of deep neural networks for weed identification in groundnut crop |
| title_short | Fuzzy rank fusion of deep neural networks for weed identification in groundnut crop |
| title_sort | fuzzy rank fusion of deep neural networks for weed identification in groundnut crop |
| topic | Deep learning Ensemble model Fuzzy logic Smart agriculture Weed classification |
| url | https://doi.org/10.1007/s43621-025-01705-9 |
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