Enhancing Agricultural Productivity through Integrated Hyperspectral Imaging and Geospatial Data Analysis

This research study examines how HSI is integrated with geospatial image processing to enhance high precision and efficiency while working on environmental and geographical data in various disciplines. Data was collected and analyzed with the help of sensors mounted on satellites and unmanned aerial vehicles (UAVs). Modern image processing tools were applied to assess this data, including spectrum unmixing, anomaly detection, and other classifications. It shows increased data quality and efficiency of operations after implementing Lean. Applying the integrated approach in line with those in agricultural applications ensured much better detection rates of stress on crops. In particular, the integrated technique improved the detection rates by $\mathbf{2 0 \%}$, increasing it from $\mathbf{7 0 \%}$ when using Hyperspectral Imaging (HSI) alone to $\mathbf{9 0 \%}$ in four months. In the field of urban planning, the control of pollution has been enhanced due to the ability to identify areas with high pollution rates. This enhancement has led to an improved efficiency of 25 percent in pollution detection compared to older methods. The study also researched and developed new algorithm approaches that increased the fusion of spectral and geographical data by improving the analysis. These developments prognosticated possibilities for better tracking and controlling within environmental science and agriculture and the prospective application of the specified fields for urban planning and catastrophe relief. Nonetheless, the outcomes expound on the revolutionary aspect of integrating HSI and GIS technology in that it infers a significant leap in the ability to address and manipulate environmental data.