Predicting Crop Water Requirements Using IoT Sensor Data for Deep Learning - Saluky, Saluky; Fatimah, Aisya (Primakara University, 2025)
The optimization of irrigation is a crucial factor in enhancing agricultural productivity and resource efficiency. This study proposes a deep learning-based approach to predict plant water requirements using data from IoT sensors. The system collects real-time environmental parameters such as soil moisture, temperature, humidity, and solar radiation, which are then processed using a deep learning model to generate accurate irrigation recommendations. The model is trained and evaluated on historical sensor data to ensure robustness and reliability in varying climatic conditions. The proposed method aims to minimize water wastage while maintaining optimal soil moisture levels, thereby improving crop health and yield. Experimental results demonstrate that the deep learning model outperforms conventional threshold-based irrigation systems in terms of prediction accuracy and water conservation. This research contributes to the advancement of smart farming by integrating IoT and artificial intelligence for precision agriculture.