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1.	Ahmed, Faisal, et al. (author) Machine Learning-Based Tomato Leaf Disease Diagnosis Using Radiomics Features 2023 In: Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering - TCCE 2022. - : Springer Science and Business Media Deutschland GmbH. - 9789811994821 - 9789811994838 ; , s. 25-35 Conference paper (peer-reviewed)abstract Tomato leaves can be infected with various infectious viruses and fungal diseases that drastically reduce tomato production and incur a great economic loss. Therefore, tomato leaf disease detection and identification are crucial for maintaining the global demand for tomatoes for a large population. This paper proposes a machine learning-based technique to identify diseases on tomato leaves and classify them into three diseases (Septoria, Yellow Curl Leaf, and Late Blight) and one healthy class. The proposed method extracts radiomics-based features from tomato leaf images and identifies the disease with a gradient boosting classifier. The dataset used in this study consists of 4000 tomato leaf disease images collected from the Plant Village dataset. The experimental results demonstrate the effectiveness and applicability of our proposed method for tomato leaf disease detection and classification.

Ahmed, Faisal, et al. (author)
Machine Learning-Based Tomato Leaf Disease Diagnosis Using Radiomics Features
2023
In: Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering - TCCE 2022. - : Springer Science and Business Media Deutschland GmbH. - 9789811994821 - 9789811994838 ; , s. 25-35
Conference paper (peer-reviewed)abstract
- Tomato leaves can be infected with various infectious viruses and fungal diseases that drastically reduce tomato production and incur a great economic loss. Therefore, tomato leaf disease detection and identification are crucial for maintaining the global demand for tomatoes for a large population. This paper proposes a machine learning-based technique to identify diseases on tomato leaves and classify them into three diseases (Septoria, Yellow Curl Leaf, and Late Blight) and one healthy class. The proposed method extracts radiomics-based features from tomato leaf images and identifies the disease with a gradient boosting classifier. The dataset used in this study consists of 4000 tomato leaf disease images collected from the Plant Village dataset. The experimental results demonstrate the effectiveness and applicability of our proposed method for tomato leaf disease detection and classification.

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