Optimizing Soil Moisture Predictions with Hybrid Computational Algorithms in Farming

Agriculture is adapting modern technologies very rapidly to increase productivity.Modern agriculture now incorporates various high-tech tools, including sensors, machinery, and information technology. These advancements in precision agriculture and robotics allow farms to function more profitably, efficiently, safely, and sustainably. One of the main aspects of increasing productivity is ensuring that the soil contains the right moisture levels. Soil moisture is the quantity of water that is present in the soil.It provides plants with the necessary nutrients, assists in transpiration, and encourages the development of strong roots. Predicting the moisture level of the soil helps in choosing a better crop for cultivation. It helps in increasing the crop yield. Soto find the accurate moisture level of the soil this work uses various machine learning algorithms such as LSTM and XG Boosting and proposes a Novel and hybrid approach. This proposed hybridalgorithm helps in accurately and efficiently calculating the Soil moisture content by evaluating and analyzing the complexities in the dataset.