The Role of Stream Data in Advancing Sustainable Agriculture Practices

Stream data might make farming more sustainable, efficient, and ecologically friendly at a time when agricultural concerns include feeding a rising population and slowing climate change. This study examines how stream data promotes sustainable farming utilizing three novel methods: Random Forest, CNN, and Reinforcement Learning. Our studies with these approaches revealed how stream data may change things. Random Forest is one of the greatest crop health algorithms because it leverages real-time sensor network data. CNN uses high-resolution images to identify and manage pests. Irrigation optimization demonstrates RL by managing water, avoiding waste, and protecting resources. Comparing the supplied approaches shows they outperform six conventional agricultural methods. These findings demonstrate how real- time data-driven strategies might enhance food health, resource consumption, and environmental impact. These adjustments are necessary for food security and climate change adaptation. Stream data is increasingly essential to healthy farms as we feed everyone and safeguard the planet. Data, algorithms, and agricultural practices that work together build a robust, ecologically responsible farm. To ensure sustainable agriculture, we must continue adopting these cutting-edge approaches. This protects our food supply and global health.