Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops
Abstract Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly cr...
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| Format: | Article |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-74127-8 |
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| author | Tariq Ali Saif Ur Rehman Shamshair Ali Khalid Mahmood Silvia Aparicio Obregon Rubén Calderón Iglesias Tahir Khurshaid Imran Ashraf |
| author_facet | Tariq Ali Saif Ur Rehman Shamshair Ali Khalid Mahmood Silvia Aparicio Obregon Rubén Calderón Iglesias Tahir Khurshaid Imran Ashraf |
| author_sort | Tariq Ali |
| collection | DOAJ |
| description | Abstract Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses. |
| format | Article |
| id | doaj-art-059eb6b498274a568f5f236724e8fa2c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-059eb6b498274a568f5f236724e8fa2c2024-12-08T12:31:21ZengNature PortfolioScientific Reports2045-23222024-12-0114111610.1038/s41598-024-74127-8Smart agriculture: utilizing machine learning and deep learning for drought stress identification in cropsTariq Ali0Saif Ur Rehman1Shamshair Ali2Khalid Mahmood3Silvia Aparicio Obregon4Rubén Calderón Iglesias5Tahir Khurshaid6Imran Ashraf7University Institute of Information Technology, PMAS–Arid Agriculture UniversityUniversity Institute of Information Technology, PMAS–Arid Agriculture UniversityUniversity Institute of Information Technology, PMAS–Arid Agriculture UniversityInstitute of Computing and Information Technology, Gomal UniversityUniversidad Europea del Atlántico.Universidad Europea del Atlántico.Department of Electrical Engineering, Yeungnam UniversityDepartment of Information and Communication Engineering, Yeungnam UniversityAbstract Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses.https://doi.org/10.1038/s41598-024-74127-8Plant scienceMachine learningDeep learningSimple modular architecture research tool (SMART)Plant drought stress |
| spellingShingle | Tariq Ali Saif Ur Rehman Shamshair Ali Khalid Mahmood Silvia Aparicio Obregon Rubén Calderón Iglesias Tahir Khurshaid Imran Ashraf Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops Scientific Reports Plant science Machine learning Deep learning Simple modular architecture research tool (SMART) Plant drought stress |
| title | Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops |
| title_full | Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops |
| title_fullStr | Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops |
| title_full_unstemmed | Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops |
| title_short | Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops |
| title_sort | smart agriculture utilizing machine learning and deep learning for drought stress identification in crops |
| topic | Plant science Machine learning Deep learning Simple modular architecture research tool (SMART) Plant drought stress |
| url | https://doi.org/10.1038/s41598-024-74127-8 |
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