Feature fusion based artificial neural network model for disease detection of bean leaves

Eray Önler; Eray Önler

doi:10.3934/era.2023122

Electronic Research Archive

2023, Volume 31, Issue 5: 2409-2427. doi: 10.3934/era.2023122

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Research article

Feature fusion based artificial neural network model for disease detection of bean leaves

Eray Önler ^,

Tekirdag Namik Kemal University, Faculty of Agriculture, Biosystem Engineering Department, Suleymanpasa, Tekirdag, Turkey

Received: 19 December 2022 Revised: 16 February 2023 Accepted: 23 February 2023 Published: 28 February 2023

Plant diseases reduce yield and quality in agricultural production by 20–40%. Leaf diseases cause 42% of agricultural production losses. Image processing techniques based on artificial neural networks are used for the non-destructive detection of leaf diseases on the plant. Since leaf diseases have a complex structure, it is necessary to increase the accuracy and generalizability of the developed machine learning models. In this study, an artificial neural network model for bean leaf disease detection was developed by fusing descriptive vectors obtained from bean leaves with HOG (Histogram Oriented Gradient) feature extraction and transfer learning feature extraction methods. The model using feature fusion has higher accuracy than only HOG feature extraction and only transfer learning feature extraction models. Also, the feature fusion model converged to the solution faster. Feature fusion model had 98.33, 98.40 and 99.24% accuracy in training, validation, and test datasets, respectively. The study shows that the proposed method can effectively capture interclass distinguishing features faster and more accurately.
- leaf disease,
- artificial neural network,
- feature fusion,
- deep neural networks,
- computer vision
Citation: Eray Önler. Feature fusion based artificial neural network model for disease detection of bean leaves[J]. Electronic Research Archive, 2023, 31(5): 2409-2427. doi: 10.3934/era.2023122

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Abstract

Plant diseases reduce yield and quality in agricultural production by 20–40%. Leaf diseases cause 42% of agricultural production losses. Image processing techniques based on artificial neural networks are used for the non-destructive detection of leaf diseases on the plant. Since leaf diseases have a complex structure, it is necessary to increase the accuracy and generalizability of the developed machine learning models. In this study, an artificial neural network model for bean leaf disease detection was developed by fusing descriptive vectors obtained from bean leaves with HOG (Histogram Oriented Gradient) feature extraction and transfer learning feature extraction methods. The model using feature fusion has higher accuracy than only HOG feature extraction and only transfer learning feature extraction models. Also, the feature fusion model converged to the solution faster. Feature fusion model had 98.33, 98.40 and 99.24% accuracy in training, validation, and test datasets, respectively. The study shows that the proposed method can effectively capture interclass distinguishing features faster and more accurately.

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