This study tackles the important issue of plant conservation by highlighting the need to monitor vegetation health and diversity in agroecosystems. These areas face threats from environmental pressures like pollution, habitat loss, and climate change, which impact plant stability. To improve vegetation assessment, this research uses remote sensing tools, specifically the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI), to create strong LAI estimation models for wheat. Field experiments conducted over two crop seasons at G.B. Pant University employed a Split-Split-Plot Design with different sowing dates, irrigation levels, and varieties to capture a wide range of canopy conditions. A variety of regression models, including linear, exponential, logarithmic, power, and sigmoid models, were created and assessed. Machine-learning methods, such as Support Vector Regression and Random Forest Regression, were also explored to improve predictive accuracy. The modeling faced challenges due to NDVI saturation at high canopy density, seasonal changes in microclimate, and complex interactions among treatments. These issues required careful calibration and validation of the models. Results showed that non-linear models, especially sigmoid regression, best represented the NDVI-LAI relationship, achieving high coefficients of determination (R² = 0.8625 for training and 0.9213 for validation). Meanwhile, machine-learning models also performed well with complex data structures. Overall, the study provides valuable insights into crop monitoring using remote sensing, offering better tools for precision agriculture, efficient water use, and long-term plant biodiversity conservation.