An Enhanced Hybrid Machine Learning Model for Plant Disease Detection and Classification
Mara Măcelaru, Petrică C. Pop, Rareș Chiuzbăian, Norbert Kovacs

Abstract. Timely and precise detection of plant diseases plays a crucial role in ensuring good agricultural productivity and food security. Conventional methods of disease detection frequently depend on manual inspection, which may be time-consuming and susceptible to errors. In our paper, we develop an enhanced hybrid machine learning (ML) based model that combines Bayesian Convolutional Neural Networks (B-CNNs) for feature extraction with Gaussian Naïve Bayes (GNB) classification for final decision-making. In addition, we performed various data augmentation methods to strengthen the diversity of the training data and to improve its generalization. Our proposed hybrid ML-based model was trained and validated on the PlantVillage dataset. The performance metrics obtained were impressive, proving that it is highly competitive against existing state-of-the-art solution approaches and demonstrating the high potential of our hybrid ML-based model for real-world applications in smart agriculture.

Keywords: plant disease detection and classification; machine learning; hybrid machine learning; Bayesian convolutional neural networks; Gaussian naïve Bayes; data augmentation; PlantVillage

Mara Măcelaru, Petrică C. Pop, Rareș Chiuzbăian, Norbert Kovacs, "An Enhanced Hybrid Machine Learning Model for Plant Disease Detection and Classification", Proc. 20th Int. Conf. on Hybrid Artificial Intelligence Systems (HAIS 2025), Lecture Notes in Computer Science, vol. 16202, Springer, Cham, 2026, pp. 91–102, 2023. DOI: https://doi.org/10.1007/978-3-032-08465-1_8.

Reference: Proc. 20th Int. Conf. on Hybrid Artificial Intelligence Systems (HAIS 2025), Lecture Notes in Computer Science, vol. 16202, Springer, Cham, 2026, pp. 91–102. DOI: 10.1007/978-3-032-08465-1_8

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