RAHBA-Net: Implementation of Rand Augment and Hybrid Bat Algorithm to Optimize Convolutional Neural Network Performance in Classifying Coffee Plant Diseases

Coffee is one of the most popular beverages, with approximately 1.6 billion cups consumed daily worldwide. To meet global demand, global coffee production must increase by at least 50% by 2050. However, coffee plants in every country are susceptible to pests and diseases, posing a significant challenge for nations to improve both the quality and quantity of coffee production. Leaves are the primary source for detecting diseases in coffee plants. Rust, miner, and phoma are the most common diseases that affect coffee plants. Experts often use conventional methods to identify diseases through visual inspection, but these methods are time-consuming, costly, prone to errors, and impractical. Machine learning and image processing have also been widely used in classifying coffee plant diseases, yet there is still limited implementation of image processing, with many still relying on manual tuning. Therefore, this research proposes the implementation of RandAugment and HBA to optimize the performance of CNN architectures in classifying coffee plant diseases. The proposed model achieved the best accuracy of 98.81% in the first model and the best parameters of 0.376 million in the second model, ensuring both efficiency and effectiveness in classifying coffee plant diseases.