IoT-Enabled Plant Root Monitoring for Improved Water and Nutrient Uptake with Long Short-Term Memory

This research presents a novel method for improving root monitoring in agricultural systems via the use of Long Short-Term Memory (LSTM) networks and the Internet of Things (IoT). The goal is to improve water and nutrient absorption. Ineffective resource management and lower crop yields are common results of using antiquated technologies to track plant roots, which lack both real-time data and accuracy. The proposed IoT system uses sensors to monitor soil moisture, nutrient levels, and root activity in real time, giving valuable information about plant health and growth dynamics. LSTM networks, a subset of recurrent neural networks, are trained on the acquired data to accurately forecast the root's behavior in the future by capturing its relationships throughout time. With this predictive capacity, growers can water and fertilize plants precisely according to their individual requirements, making the most efficient use of resources and reducing waste. With the help of the IoT even massive agricultural operations can be remotely monitored and controlled, allowing for proactive management and intervention. To improve water and nutrient absorption efficiency, this in turn increases crop output and helps to save the environment. It provides a viable alternative for sustainable agriculture.