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Deep feature-based plant disease identification using machine learning classifier

  • S.I. : Multifaceted Intelligent Computing Systems (MICS)
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Abstract

Identification of plant diseases plays an important and challenging role in the protection of agricultural crops and also their quality. Several works are in progress to improve the existing leaf image-based disease identification using deep learning. In this paper, we have studied some of the existing plant disease identification techniques and proposed a novel plant disease identification model based on deep convolutional neural network (CNN) along with different ensemble classifiers. In our model, features used for classification are obtained using the Deep CNN model and classified using different classifiers such as Support Vector Machine (SVM), K Nearest Neighbor, Random Forest, Naive Bayes, and Logistic Regression (LR). The obtained results are compared with different existing deep learning classifiers. The result shows that the SVM and LR classifier outperforms some of the other pre-trained deep learning models in terms of accuracy, precision, and recall. It is also observed that using significantly less number of parameters, we have achieved better classification accuracy than some pre-trained deep learning models.

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Data availability

The datasets analyzed during the current study are available in Website as Plant Village Dataset and the link is: https://github.com/spMohanty/PlantVillage-Dataset.

Code availability

Not applicable.

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Authors and Affiliations

  1. Department of Information Technology, North-Eastern Hill University, Shillong, India

    Sk Mahmudul Hassan & Arnab Kumar Maji

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  1. Sk Mahmudul Hassan
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  2. Arnab Kumar Maji
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Correspondence to Arnab Kumar Maji.

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Hassan, S.M., Maji, A.K. Deep feature-based plant disease identification using machine learning classifier. Innovations Syst Softw Eng 20, 789–799 (2024). https://doi.org/10.1007/s11334-022-00513-y

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