Paper Contents
Abstract
Coffee is one of the most widely consumed beverages globally, and its production is significantlythreatened by various leaf diseases, leading to substantial economic losses for farmers. To reducethis a deep learning-based approach for the detection of coffee leaf diseases utilizingConvolutional Neural Networks (CNNs) and transfer learning techniques are used. A diversedataset of coffee leaf images are collected, representing healthy leaves and those affected bycommon diseases, including coffee leaf rust, bacterial blight, and leaf spot. The dataset wasaugmented through techniques such as rotation, flipping, and scaling to enhance model robustness.Transfer learning with pre-trained models, specifically Densenet and ResNet, fine-tuning them onour dataset to leverage their powerful feature extraction capabilities. The suggested model wasexamined and achieving an 82.3% accuracy and primary objective is to enhance the modelsaccuracy in detecting leaf-based diseases by leveraging advanced deep learning techniques and thisis crucial for agricultural practices.
Copyright
Copyright © 2024 Sai Chandu Gedela. This is an open access article distributed under the Creative Commons Attribution License.
