A COMPUTATIONALLY EFFICIENT DEEP LEARNING APPROACH FOR LOCALIZATION AND CLASSIFICATION OF DISEASES AND PESTS IN COFFEE LEAVES

Abstract

Coffee cultivation plays a vital role in the economy of many regions worldwide, yet its productivity is frequently threatened by leaf diseases and pests that negatively impact both yield and quality. To address this challenge, automated identification of coffee leaf diseases using deep learning offers an efficient alternative to manual inspection, enabling timely intervention and improved crop management. In this study, we propose a MobileNetV2- based framework that leverages lightweight yet powerful feature extraction for real-time disease recognition. The model is trained on a newly curated dataset designed to ensure class balance and robustness, thereby improving detection accuracy across diverse disease categories. This approach provides a practical and resource-efficient solution for monitoring coffee leaf health, supporting decision-making in plantation care and contributing to sustainable disease management.