Efficient Land Use Classification for Brazilian Coffee Scenes

Machine learning in geoscience and remote sensing has reached a pivotal stage, necessitating practical implementation in real-world settings. It is crucial that these models exhibit an efficiency suitable for application in computationally limited settings, coupled with maintaining competitive accuracy levels. This paper introduces a purpose-built model designed to meet both of these requirements, surpassing contemporary models in accuracy and efficiency. The proposed model demonstrates a remarkable ability to detect subtle changes in land use images with extreme accuracy while maintaining a minimal parameter count of 34,313 (134.04 kB). This multifaceted success not only raises the bar in terms of accuracy, but also illustrates the importance of custom models for the future of machine learning.