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@article{177966,
        author = {MINUKU SHARMILA and KANDE JASWANTH and BERI MURALI and MURTHAPPAGARI AJAY and DEPURU HARIKA},
        title = {A Survey On AI-Powered Smart Farming Assistant: Transforming Agriculture with Machine Learning and Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1760-1768},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177966},
        abstract = {This research presents an IoT-based Smart Farming System designed to monitor, analyze, and optimize various agricultural processes. The system employs IoT-enabled sensors to collect real-time data on environmental parameters such as soil moisture, temperature, humidity, and light intensity, which are critical for effective crop management. This research focuses on predicting agricultural yields using a combination of regression models and deep learning approaches, which analyze complex, high-dimensional datasets derived from environmental, meteorological, and historical agricultural records. The methodology includes the application of machine learning regression techniques, such as linear regression, support vector regression, and decision tree regression, for preliminary yield predictions. The study aims to assist farmers and policymakers in making informed decisions to maximize productivity and sustainability. This research focuses on the development of a leaf-based plant disease detection system using machine learning techniques. Artificial intelligence is now being used extensively in the agricultural industry. The Agriculture sector faces various threats and challenges and to mention a few, Information on pest control techniques, Yield Maximization, inappropriate Soil treatment, Pest control system, Disease control information, Information on farm technology and innovation etc . This research explores the transformative role of AI in addressing key challenges in agriculture, such as crop management, pest control, soil monitoring, and resource optimization.},
        keywords = {Internet of things, Agricultural Technology, Smart farming, Machine learning, Artificial intelligence, Smart sensors, Deep learning, Support vector machine, Decision tree, Fog computing.},
        month = {May},
        }