Abstract
This research paper presents “AutoAgro,” a pioneering agricultural framework amalgamating IoT, machine learning, and Flutter technology to revolutionize contemporary farming. Through a network of IoT sensors, real-time environmental parameters vital for agriculture are meticulously captured. Leveraging a sophisticated machine learning model, this data is meticulously analyzed, enabling accurate prediction and early diagnosis of plant diseases. The integration of Flutter technology in our Android interface offers an intuitive platform for farmers, featuring multilingual support and instant disease alerts, fostering proactive management strategies. The paper meticulously details the development, implementation, and performance evaluation of this holistic system, showcasing its potential to optimize crop yield, mitigate losses, and promote sustainable farming practices. Through rigorous experimentation and analysis, this study delves into the transformative impact of intelligent technologies on the agricultural landscape, providing valuable insights for researchers, practitioners, and stakeholders in the field.
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Chakraborty, A., Bhattacharjee, A., Ghadge, H., Mawley, D., Paulzagde, A., Jaronde, P. (2024). A Synergistic Integration of IoT, Machine Learning, and Flutter Technology for Precise Crop Management. In: Senjyu, T., So–In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. SmartCom 2024 2024. Lecture Notes in Networks and Systems, vol 948. Springer, Singapore. https://doi.org/10.1007/978-981-97-1329-5_24
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