Sugarcane plays an essential role in global agriculture due to its significance in the production of sugar, ethanol, and bagasse. Modern sugarcane cultivation often relies on billets, making it imperative to maintain healthy billets for optimal yield. However, the use of harvesting machines introduces the potential for billet damage, which can lead to disease spread and reduced quality. Developing a robotic solution that employs computer vision and deep learning to detect the damaged billets is important. Conventional methods for damage detection are hindered by complex backgrounds, necessitating the development of an efficient model for sugarcane billet damage categorization. The work presents the Sugarcane Billet Damage Detection App, which integrates advanced image processing techniques and the FDHOA-based DMN model. The applications user-friendly interface includes informative content on sugarcane cultivation and a robust billet damage detection feature. The FDHOA-based DMN model, leveraging Fractional Calculus and optimization algorithms, achieves remarkable accuracy in categorizing sugarcane billet damage, contributing to more efficient and intuitive sugarcane harvesters.