This project introduces the EfficientVisionTransformer (EVT), a deep learning model designed for precise prediction of the Fractional Vegetation Index (FVI) in vegetation monitoring. In order to overcome the limitations of existing methods, EVT incorporates resilient attention mechanisms and streamlined computations, resulting in high accuracy while utilizing fewer computer resources. Its architecture, which includes patch embedding, Transformer encoder layers, and a linear decoder, enables the rapid processing of local information and the comprehensive creation of global context, leading to accurate Vegetation
Index predictions. Its ability to process high-resolution aerial images opens up new avenues for monitoring vegetation health and productivity across vast landscapes, providing crucial insights for sustainable land management practices. With 17 million parameters compactly packed in 155 MB and lightning-fast CPU inference duration of 606 milliseconds, EVT demonstrates exceptional efficiency, providing near-real-time insights for precision agriculture and environmental monitoring. The model's computational efficiency and low resource requirements make it accessible to a wide range of users, from researchers and environmental
agencies to agricultural organizations and individual farmers, enabling data-driven decision-making on a broader scale. EVT's exceptional accuracy is demonstrated by evaluation measures (Mean Absolute Error of 0.03823, Root Mean Square Error of 0.04582, R-squared value of 0.98556), confirming its reliability across various assessment frameworks and geographical scales. The EVT model represents a groundbreaking solution that offers a future where precision and efficiency come together to support informed decision-making for ecosystem health and sustainable land management.