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@article{183227,
        author = {BANDI DOSS and M.PAPA and B.Thanuja},
        title = {SMART FARMING -AI POWERED CROP YIELD OPTIMISATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {979-984},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183227},
        abstract = {Crop Health Monitoring is crucial for maximizing agricultural productivity. In this regard, the integration of machine learning has proven to be a game-changer. This project focuses on developing a system capable of producing high yield using machine learning, particularly by using IOT. Field Health Surveillance plays a key role in optimizing agricultural output. Monitoring the Health of Crops is essential for achieving high agricultural yields. Artificial intelligence (AI) has become a transformative tool in this field. This study proposes a machine learning-based system that detects and classifies leaf diseases, specifically an IOT for analysing soil parameters including PH, moisture, humidity and temperature. By using above parameters, the farmers can monitor the field conditions remotely by reading the collected values of sensors and actuators which are transmitted to the server using an Arduino microcontroller and wi-fi module. To predict health of the crop and to guide the farmers and sowing reasonable crops by deploying machine learning. This helps which crop has to be yielded based on the seasons or climatic conditions along with the yield produced with the available soil factors. Thus, the developed model can have better accuracy and crop prediction.},
        keywords = {Agriculture, Artificial Intelligence, Monitoring, Control, Prediction, Features, Convolutional Neural Network, Machine Learning.},
        month = {August},
        }