Optimizing Accuracy of Image-Based Crop Leaf Disease Detection using DenseNet121 Deep Learning Model
Keywords:
DenseNet121, Deep Learning, Crop Diseases, Image ProcessingAbstract
Crop diseases are one of the major challenges affecting agricultural productivity and food security, as they can significantly reduce crop quality and yield. Early and accurate identification of leaf diseases is therefore essential for timely disease management and effective crop protection. Traditional disease identification methods mainly depend on manual observation by farmers or agricultural experts, which can be time-consuming, subjective, and difficult to implement on a large scale. To address these limitations, this study proposes an image-based deep learning approach for automated crop leaf disease detection using the DenseNet121 architecture. DenseNet121 is selected because its dense connectivity mechanism enables effective feature reuse and improved information propagation across network layers, making it suitable for extracting discriminative visual features from leaf images. The proposed framework includes image preprocessing, data augmentation, feature extraction, model training, and disease classification. To optimize the detection performance, the model training parameters and learning process are carefully tuned to improve classification accuracy and generalization capability. The performance of the proposed DenseNet121 model is evaluated using standard metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results demonstrate the effectiveness of the proposed deep learning framework in accurately distinguishing healthy and diseased crop leaves. The optimized DenseNet121 model provides a reliable and automated solution for crop disease detection and can support farmers and agricultural professionals in early disease diagnosis. The proposed approach has significant potential for integration into smart agriculture and precision farming systems, contributing to improved crop health, reduced disease-related losses, and enhanced agricultural productivity.
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