PREDICTING GERM DEVELOPMENT CONDITIONS ON CROPS USING REAL-TIME IOT DATA AND ANN MODEL - Meshram, Susmita A.; Choudhari, N.K (ShodhKosh: Journal of Visual and Performing Arts, 2023)
The prediction of germ development conditions on crops is critical in optimizing crop yields and ensuring sustainable agriculture. This study presents a MATLAB-based artificial neural network (ANN) model for predicting germ development conditions in crops using IoT-based weather data. The research utilizes feedforward neural networks (FNN) algorithms for training, leveraging historical pattern-based weather data collected from IoT sensors for temperature, humidity, moisture, and light intensity (LDR). The IoT system provides real-time data streams, enabling the ANN models to predict optimal conditions for germ development with high accuracy. The FNN model learns complex relationships between environmental factors and germ development. The proposed system achieves robust performance in scenarios with varying weather conditions, making it a reliable tool for farmers and agricultural planners. The results demonstrate the feasibility of using IoT-enabled systems and ANN models for agricultural applications, showing significant potential for scalability and integration into smart farming systems.