Application of Machine Learning Algorithms for Predictive Modeling of Climate-Smart Agriculture
DOI:
https://doi.org/10.64137/XXXXXXXX/IJAES-V1I1P103Keywords:
Machine learning, Climate-smart agriculture, Predictive modeling, Crop yield prediction, Precision agriculture, Ensemble learning, IoT integration, Sustainability, Resource optimization, Climate resilienceAbstract
Machine learning algorithms are key to advancing CSA since they allow predictive modeling to help use resources better, strengthen farm defenses and improve crop yields. Researchers have found that AdaBoost Regressor and Random Forest can accurately predict yields in farming (MAE: 0.22, R²: 0.89). This is based on analyzing both historical and real-time weather, soil data and farming practices. Gradient Boosting helped predict how farmers will use CSA by integrating various important factors like their background, farming methods and climate, supporting suitable interventions. Machine learning approaches such as ViT-B16 and ResNet-50 enhance predictions by using images to watch over crop health, reaching F1-scores of up to 99.54%. Using IoT devices and remote sensing results in more accurate data, so there are better ways to irrigate and get advanced notice of serious weather events. Even so, difficulties arise, for example, inconsistent quality of data, scaling issues with models and the requirement for colleagues from many disciplines to tackle the problems related to society and technology. Because of ML methods, these agricultural techniques assist with both efficient resource use and adaptation to climate challenges
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