Plant Disease Recognition Based on Leaf Images Using Sequential-Based DenseNet Architecture

Abstract

Plant diseases that affect leaves can significantly reduce crop quality and productivity, making accurate and efficient detection methods essential. This study aims to develop a plant disease recognition model based on leaf images using a sequential DenseNet121 architecture. The dataset consists of 1,530 leaf images categorized into three classes: Healthy, Powdery, and Rust, which are divided into training, validation, and testing sets with a relatively balanced distribution. The model employs DenseNet121 as a base model with pre-trained ImageNet weights, where all base layers are frozen to function as a feature extractor. The classification process utilizes GlobalAverage Pooling2D, Dense, Dropout, and Softmax layers. Experimental results show that the model achieves an accuracy of 98.28% on the training data and 96.25% on the validation data. Evaluation on the test dataset yields an accuracy of 93.33%, indicating that the proposed model demonstrates good generalization capability in classifying plant diseases based on leaf images. These results suggest that the sequential DenseNet architecture is effective for plant disease recognition and has potential for further development as a decision support system in agriculture