Coffee disease detection and classification using image processing: A Literature review

Abstract

Coffee, as one of the world's most consumed beverages, sustains livelihoods for millions across more than 50 nations. The vulnerability of coffee plants to diseases, particularly Coffee Leaf Rust and Coffee Berry Disease, poses a significant threat to global production and quality. Leveraging advancements in image processing and computer vision, researchers have explored diverse classification algorithms, ranging from traditional Support Vector Machines to state-of-the-art Deep Convolutional Neural Networks (DCNNs). This review literature addresses the challenges of coffee disease detection, emphasizing the need for precise and early identification. Notable studies have achieved commendable accuracies, such as SVMs reaching 96% and DCNNs demonstrating precision but with extended training times. Innovations like feature concatenation, transfer learning, and ensemble methods have emerged as strategies to overcome classification limitations. Recent breakthroughs showcase impressive results, including DenseNet models achieving a classification accuracy of 99.57% and MobileNetV2 reaching 99.93%. Additionally, Convolutional Neural Networks and VGG-19 architecture demonstrated a promising F1-Score of 90% in classifying various coffee leaf diseases. This concludes with a vision for ongoing advancements, emphasizing the fusion of image processing and machine learning technologies to safeguard the global coffee industry by enabling early and accurate disease detection.