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This is a Quantitative Study study.

September 27, 2025Open Access

Advancing plant disease detection: A comparative analysis of deep learning and hybrid machine learning models

RKRajesh KumarChaudhary Devi Lal University VSVikram SınghChaudhary Devi Lal University

Puntos clave

The deep learning model achieved an accuracy of 97.57%, indicating its effectiveness with large datasets.
The hybrid model, using SVM classifiers, attained 91.73% accuracy while ensuring interpretability and efficiency.
Performance was measured using metrics like accuracy, precision, recall, and F1-score, demonstrating the models' capabilities.
Applications include online monitoring with drones and diagnostic tools for farmers, enhancing crop production.

Resumen

Detecting plant diseases is essential for precision agriculture, as it enhances crop production and ensures the security of the food supply. We adopted two methods for this research: a method based on deep learning, through Convolutional Neural Networks (CNN), and a hybrid model using classical machine learning. The dataset comprised images of plant leaves from Kirtan village in Hisar, Haryana, which were annotated by plant pathologists. The CNN model, which autonomously extracts hierarchical spatial features, achieved an accuracy of 97.57%, making it ideal for large datasets. Conversely, the Hybrid model utilizing handcrafted GLCM and LBP features and SVM classifiers achieved 91.73% accuracy while providing interpretability and computational efficiency in resource limited setups. The performance of the models was measured in terms of accuracy, precision, recall and F1-score. Applications range from on-line monitoring with drones to diagnostic equipment for the farmer.

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