QGAPHnet : Quantum Genetic Algorithm Based Hybrid QLSTM Model for Soil Moisture Estimation

Soil moisture, pH, soil temperature, humidity among other factors play a pivotal role in affecting the agricultural productivity of a region, influencing factors such as crop yield, organic carbon estimation, and crop growth analysis. This paper introduces a comprehensive investigation into soil moisture and temperature dynamics, employing a dynamic soil moisture dataset. Utilising Quantum Long Short Term Memory (QLSTM), we apply Quantum Genetic Algorithm (QGA) and Particle Swarm Optimisation (PSO) to study and predict patterns within the dataset. Our approach not only enhances the precision of soil moisture estimations but also provides a novel perspective on environmental factors. The findings from this study hold significant implications for understanding and managing soil moisture in diverse contexts, spanning agriculture, hydrology, and ecosystem studies.