<![CDATA[Background: Traditional approaches to rice disease identification depend mainly upon visual examination, which is quite labor-intensive and generally demands a certain skill level from people engaged in this activity. However, these approaches suffer from high time costs and potential errors and are impractical for large-scale daily monitoring. The recent rise of deep learning has offered opportunities for automated detection process improvement, which needs to be fast-accurate as good farmer-centric. Objective: This study aims to enhance the accuracy of image rice leaf disease classification via feature extraction for rice leaf disease in four instances of pre-trained CNN models and provide an automated solution for early detection ahead of timely care by obtaining insights into crop production through precision agriculture. Methods: This study combined transfer learning with four pre-trained CNN models - InceptionResNetV2, MobileNetV2, DenseNet121, and VGG16. Results: The outcome of this research enables the identification of the optimal model to relate datasets where DenseNet121 achieved the highest accuracy, i.e. 99.10%, followed by MobileNetV2, having a precision of 97.10%. Conclusion: The new framework results in a highly accurate and high-throughput early disease detection element in precision agriculture, better than state-of-the-art approaches based on traditional techniques. Keywords: Deep Learning, DenseNet121, Image Classification, Rice Leaf Diseases, Transfer Learning]]>
