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@article{173914,
        author = {Dr. Sheetal Dhande and anuj nirmal and yash tayade and lakhan panpaliya and abhishek watmode and prathmesh chavhan},
        title = {Machine Learning In Agriculture: A Review Of Farming & Fertilizer Methods},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2598-2605},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173914},
        abstract = {Agriculture 4.0 is characterized by the inlaying of advanced technologies like ML, data science, and IoT to improve agricultural yield, sustainability, and efficient resource utilization. Hence, ML can be applied on a large volume of agricultural data to analyse for yield prediction, efficient resource utilization, and evidence-based decision-making. IoT allows for real-time monitoring-interconnected devices like sensors, drones, and satellites that, in turn, allow for efficient management of farming processes. The paper discussed different applications of ML, data science, and IoT in agriculture, ranging from fertilizer application optimization and pest/disease detection to irrigation management and soil health monitoring. Farmers can, therefore, improve yields and minimize harm to the environment by making use of these technologies and, to some extent, contributing to solving global food challenges. The study further outlines emerging trends and challenges, and describes in detail how computational intelligence can be used to turn modern agriculture more resilient and sustainable.},
        keywords = {AI in Agriculture, Crop Yield Prediction, Fertilizer Management, Machine Learning in Agriculture.},
        month = {March},
        }