International Journal of Science, Engineering and TechnologyAuthors: Associate Professor V.Pavani,, Kongara Triveni,, Evuri Bhavya Sri,, Palapala Pranathi,, Jogi Keerthi
Abstract: One of the biggest challenges fed by climate change and environmental issues are global food production. This has led to the demand of a sustainable food source as the main driver for transforming agriculture into more eco-friendly practices. In this context, the research emphasizes IoT-RS Integrated Smart Yield Prediction Model (IRSYPM) is a single intelligent system to combine the Internet of Things (IoT) sensor networks and Remote Sensing (RS) data for yield prediction and precision agriculture assistance. The model employs current data that IoT devices record as they monitor the soil, air, and nutrient content. At the same time, remote sensing from either drone or satellite is used to derive the multispectral vegetation indices like NDVI, EVI, and LAI. All these different data inputs come together on a cloud platform that uses machine learning algorithms such as Random Forest, Support Vector Regression (SVR), and Artificial Neural Networks (ANN) to predict yield outcomes.The model IRSYPM is structurally ₂ emissions by 19%. Such effects are a great contribution to eco-friendly and data-driven agricultural practices.Essentially, the IRSYPM scheme is a large-scale, secure, and intelligent framework that revolutionizes the conventional agri-food system into a smart, resilient, and environmentally friendly one.
