PLANT DISEASE DETECTION AND GROWTH ANALYSIS THROUGH MACHINE LEARNING AND IOT

Abstract

Smart agriculture is undergoing a transformative evolution through the integration of machine learning algorithms for the early identification of plant diseases. High-resolution images, facilitated by Internet of Things (IoT) devices, serve as the primary data source, meticulously analyzed by sophisticated ML models. This streamlined process allows for swift intervention, averting potential crop devastation. Complementing this imagery analysis, real-time environmental and soil data are continuously collected, offering valuable insights into the most favorable conditions for crop growth. Accessible via web or mobile applications, farmers benefit from instant access to this wealth of information, empowering them to make informed decisions and proactively mitigate risks. By leveraging this advanced system, the agricultural sector aims to enhance food production while simultaneously minimizing water and chemical usage, ushering in a more sustainable and efficient era in farming practices.