Optimized sequential model for superior classification of plant disease
Abstract Indian agriculture is vital sector in the country’s economy, providing employment and sustenance to millions of farmers. However, Plant diseases are a serious risk to crop yields and farmers’ livelihoods. Traditional plant disease diagnosis methods rely heavily on human expertise, which can...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86427-8 |
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| author | Yogesh Chimate Sangram Patil K. Prathapan Jaydeep Patil Jayendra Khot |
| author_facet | Yogesh Chimate Sangram Patil K. Prathapan Jaydeep Patil Jayendra Khot |
| author_sort | Yogesh Chimate |
| collection | DOAJ |
| description | Abstract Indian agriculture is vital sector in the country’s economy, providing employment and sustenance to millions of farmers. However, Plant diseases are a serious risk to crop yields and farmers’ livelihoods. Traditional plant disease diagnosis methods rely heavily on human expertise, which can lead to inaccuracies due to the invisible nature of early disease symptoms and the labor-intensive process, making them inefficient for large-scale agricultural management. To recover from this and, address these challenges, this study explores deep learning, specifically Convolutional Neural Networks (CNN), as a means to enhance the accuracy and efficiency of plant disease detection. Deep learning architectures, like convolutional neural network, can autonomously learn and extract complicated characteristics and patterns from huge datasets. Our research, conducted on mango and groundnut leaves collected during field visits in western Maharashtra and supplemented by online datasets, demonstrates a CNN model that achieves an impressive 96% accuracy as compared to machine learning techniques that follow tedious feature extraction. Furthermore, image processing contributes to enhancing the dataset through normalization, resizing, and augmentation for better classification results. Overall, CNN can continuously improve and adapt its performance through iterative training, resulting in higher accuracy rates and reduced false positives in contrast to conventional machine learning methods. |
| format | Article |
| id | doaj-art-695821c491ad45febf8bfa97ff34851b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-695821c491ad45febf8bfa97ff34851b2025-02-02T12:23:47ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-86427-8Optimized sequential model for superior classification of plant diseaseYogesh Chimate0Sangram Patil1K. Prathapan2Jaydeep Patil3Jayendra Khot4Department of Computer Science and Engineering, D. Y. Patil Agriculture and Technical UniversityD. Y. Patil Agriculture and Technical UniversityD. Y. Patil Agriculture and Technical UniversityD. Y. Patil Agriculture and Technical UniversityD. Y. Patil Agriculture and Technical UniversityAbstract Indian agriculture is vital sector in the country’s economy, providing employment and sustenance to millions of farmers. However, Plant diseases are a serious risk to crop yields and farmers’ livelihoods. Traditional plant disease diagnosis methods rely heavily on human expertise, which can lead to inaccuracies due to the invisible nature of early disease symptoms and the labor-intensive process, making them inefficient for large-scale agricultural management. To recover from this and, address these challenges, this study explores deep learning, specifically Convolutional Neural Networks (CNN), as a means to enhance the accuracy and efficiency of plant disease detection. Deep learning architectures, like convolutional neural network, can autonomously learn and extract complicated characteristics and patterns from huge datasets. Our research, conducted on mango and groundnut leaves collected during field visits in western Maharashtra and supplemented by online datasets, demonstrates a CNN model that achieves an impressive 96% accuracy as compared to machine learning techniques that follow tedious feature extraction. Furthermore, image processing contributes to enhancing the dataset through normalization, resizing, and augmentation for better classification results. Overall, CNN can continuously improve and adapt its performance through iterative training, resulting in higher accuracy rates and reduced false positives in contrast to conventional machine learning methods.https://doi.org/10.1038/s41598-025-86427-8Plant diseaseMachine learningFeature extractionDisease classification |
| spellingShingle | Yogesh Chimate Sangram Patil K. Prathapan Jaydeep Patil Jayendra Khot Optimized sequential model for superior classification of plant disease Scientific Reports Plant disease Machine learning Feature extraction Disease classification |
| title | Optimized sequential model for superior classification of plant disease |
| title_full | Optimized sequential model for superior classification of plant disease |
| title_fullStr | Optimized sequential model for superior classification of plant disease |
| title_full_unstemmed | Optimized sequential model for superior classification of plant disease |
| title_short | Optimized sequential model for superior classification of plant disease |
| title_sort | optimized sequential model for superior classification of plant disease |
| topic | Plant disease Machine learning Feature extraction Disease classification |
| url | https://doi.org/10.1038/s41598-025-86427-8 |
| work_keys_str_mv | AT yogeshchimate optimizedsequentialmodelforsuperiorclassificationofplantdisease AT sangrampatil optimizedsequentialmodelforsuperiorclassificationofplantdisease AT kprathapan optimizedsequentialmodelforsuperiorclassificationofplantdisease AT jaydeeppatil optimizedsequentialmodelforsuperiorclassificationofplantdisease AT jayendrakhot optimizedsequentialmodelforsuperiorclassificationofplantdisease |