Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization
Optimal agricultural methods need precise crop health and ecological strain monitoring. This study proposes a novel data science strategy to improve crop health prediction and stress assessment. ResXceNet-HBA is a cutting-edge classification model that uses ResNet blocks, Xception modules with Adapt...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10843189/ |
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| author | Eman Ali Aldhahri Abdulwahab Ali Almazroi Monagi Hassan Alkinani Nasir Ayub Elham Abdullah Alghamdi Nourah Fahad Janbi |
| author_facet | Eman Ali Aldhahri Abdulwahab Ali Almazroi Monagi Hassan Alkinani Nasir Ayub Elham Abdullah Alghamdi Nourah Fahad Janbi |
| author_sort | Eman Ali Aldhahri |
| collection | DOAJ |
| description | Optimal agricultural methods need precise crop health and ecological strain monitoring. This study proposes a novel data science strategy to improve crop health prediction and stress assessment. ResXceNet-HBA is a cutting-edge classification model that uses ResNet blocks, Xception modules with Adaptive Depthwise Separable Convolutions, and HBA-optimized parameters. This model uses HBA’s Dynamic Exploration-Exploitation Balance-fine-tuned Dynamic Feature Recalibration and adaptive convolutions. Imputation Weight Crop Labels (WICL) to accurately fill in missing data, Localised Feature Scaling (LFS) and Adaptive Feature Discretization (AFD) to standardize and categorize features, and the Environmental Stress Factor (ESF) to evaluate crop stress address data problems ASRFS and Crop-Specific Environmental Impact Weighting increase model performance. Our system also employs Adaptive Synthetic Resampling with Environmental Context. Using novel measures including the Crop Type Generalisation Score (CTGS) and Environmental Sensitivity Index (ESI), the ResXceNet-HBA model achieved 98.5% accuracy, 98.2% precision, 98.7% recall, and 98.4% F1-Score. These results beat ResNet, CNN, and Inception V2. The model executed in 50.9 seconds, faster than the alternatives. The confusion matrix exhibits minimal false positives and negatives, suggesting good prediction accuracy. ResXceNet-HBA’s statistics and resource optimization value increases. Precision farming and sustainable agriculture benefit from our strategy’s significant environmental stress and crop health assessments. |
| format | Article |
| id | doaj-art-16e38cd154e04479afb4ea5c0eee3173 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-16e38cd154e04479afb4ea5c0eee31732025-08-20T02:13:31ZengIEEEIEEE Access2169-35362025-01-0113723757238810.1109/ACCESS.2025.353000610843189Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource OptimizationEman Ali Aldhahri0https://orcid.org/0000-0002-4233-4610Abdulwahab Ali Almazroi1https://orcid.org/0000-0001-7181-2100Monagi Hassan Alkinani2https://orcid.org/0000-0002-7658-7085Nasir Ayub3https://orcid.org/0000-0002-1153-5401Elham Abdullah Alghamdi4Nourah Fahad Janbi5https://orcid.org/0000-0001-7330-1566Department of Computer Science and Artificial Intelligence, Collage of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaDepartment of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, Collage of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaDepartment of Creative Technologies, Air University Islamabad, Islamabad, PakistanDepartment of Computer Science and Artificial Intelligence, Collage of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaDepartment of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah, Saudi ArabiaOptimal agricultural methods need precise crop health and ecological strain monitoring. This study proposes a novel data science strategy to improve crop health prediction and stress assessment. ResXceNet-HBA is a cutting-edge classification model that uses ResNet blocks, Xception modules with Adaptive Depthwise Separable Convolutions, and HBA-optimized parameters. This model uses HBA’s Dynamic Exploration-Exploitation Balance-fine-tuned Dynamic Feature Recalibration and adaptive convolutions. Imputation Weight Crop Labels (WICL) to accurately fill in missing data, Localised Feature Scaling (LFS) and Adaptive Feature Discretization (AFD) to standardize and categorize features, and the Environmental Stress Factor (ESF) to evaluate crop stress address data problems ASRFS and Crop-Specific Environmental Impact Weighting increase model performance. Our system also employs Adaptive Synthetic Resampling with Environmental Context. Using novel measures including the Crop Type Generalisation Score (CTGS) and Environmental Sensitivity Index (ESI), the ResXceNet-HBA model achieved 98.5% accuracy, 98.2% precision, 98.7% recall, and 98.4% F1-Score. These results beat ResNet, CNN, and Inception V2. The model executed in 50.9 seconds, faster than the alternatives. The confusion matrix exhibits minimal false positives and negatives, suggesting good prediction accuracy. ResXceNet-HBA’s statistics and resource optimization value increases. Precision farming and sustainable agriculture benefit from our strategy’s significant environmental stress and crop health assessments.https://ieeexplore.ieee.org/document/10843189/Crop health monitoringenvironmental stress assessmentResXceNet-HBAdata imputationfeature selectionprecision agriculture |
| spellingShingle | Eman Ali Aldhahri Abdulwahab Ali Almazroi Monagi Hassan Alkinani Nasir Ayub Elham Abdullah Alghamdi Nourah Fahad Janbi Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization IEEE Access Crop health monitoring environmental stress assessment ResXceNet-HBA data imputation feature selection precision agriculture |
| title | Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization |
| title_full | Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization |
| title_fullStr | Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization |
| title_full_unstemmed | Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization |
| title_short | Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization |
| title_sort | smart farming enhancing urban agriculture through predictive analytics and resource optimization |
| topic | Crop health monitoring environmental stress assessment ResXceNet-HBA data imputation feature selection precision agriculture |
| url | https://ieeexplore.ieee.org/document/10843189/ |
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