Mango leaf disease classification using hybrid Coyote-Grey Wolf optimization tuned neural network model

Seetha, J.; Ramanathan, Ramakrishnan; Goyal, Vishal; Tholkapiyan, M.; Karthikeyan, C.; Kumar, Ravi

doi:10.1007/s11042-023-16964-9

Multimed Tools Appl

2023

DOI: 10.1007/s11042-023-16964-9

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Mango leaf disease classification using hybrid Coyote-Grey Wolf optimization tuned neural network model

J. Seetha,

Ramakrishnan Ramanathan,

Vishal Goyal

et al.

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2024

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Cited by 3 publications

References 33 publications

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Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition

Patel,

Choudhary,

Chouhan

et al. 2024

Computers and Electrical Engineering

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Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition

Patel,

Choudhary,

Chouhan

et al. 2024

Computers and Electrical Engineering

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Advancing mango leaf variant identification with a robust multi-layer perceptron model

Fahim-Ul-Islam,

Chakrabarty,

Rahman

et al. 2024

Sci Rep

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Mango, often regarded as the “king of fruits,” holds a significant position in Bangladesh’s agricultural landscape due to its popularity among the general population. However, identifying different types of mangoes, especially from mango leaves, poses a challenge for most people. While some studies have focused on mango type identification using fruit images, limited work has been done on classifying mango types based on leaf images. Early identification of mango types through leaf analysis is crucial for taking proactive steps in the cultivation process. This research introduces a novel multi-layer perceptron model called WaveVisionNet, designed to address this challenge using mango leaf datasets collected from five regions in Bangladesh. The MangoFolioBD dataset, comprising 16,646 annotated high-resolution images of mango leaves, has been curated and augmented to enhance robustness in real-world conditions. To validate the model, WaveVisionNet is evaluated on both the publicly available dataset and the MangoFolioBD dataset, achieving accuracy rates of 96.11% and 95.21%, respectively, outperforming state-of-the-art models such as Vision Transformer and transfer learning models. The model effectively combines the strengths of lightweight Convolutional Neural Networks and noise-resistant techniques, allowing for accurate analysis of mango leaf images while minimizing the impact of noise and environmental factors. The application of the WaveVisionNet model for automated mango leaf identification offers significant benefits to farmers, agricultural experts, agri-tech companies, government agencies, and consumers by enabling precise diagnosis of plant health, enhancing agricultural practices, and ultimately improving crop yields and quality.

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A Coyote-Inspired Approach for Systemic Lupus Erythematosus Prediction Using Neural Networks

Mummaneni,

Dodda,

Ginjupalli

2024

IAPGOS

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Systemic Lupus Erythematosus (SLE) is a complicated autoimmune disease that can present with a variety of clinical symptoms, making precise prognosis difficult. Because SLE has a wide range of symptoms and may overlap with other autoimmune and inflammatory disorders, making a diagnosis can be challenging. This study creates a precise and accurate model for the prediction of SLE using the GEO dataset. For cost-effective data collection and analysis, feature selection might be essential in some applications, particularly in healthcare and scientific research. The strength of Artificial Neural Networks (ANN) for Systemic Lupus Erythematosus prediction and the Coyote Optimization Algorithm (COA) for feature selection are combined in this study. The COA is an optimization method influenced by nature and coyote hunting behavior. This study attempts to improve the effectiveness of subsequent predictive modeling by using COA to identify a subset of significant features from high-dimensional datasets linked to SLE. A Multi-layer Feed-forward Neural Network, a potent machine learning architecture renowned for its capacity to discover complex patterns and correlations within data, is then given the chosen features. Because the neural network is built to capture SLE's intricate and non-linear structure, it offers a reliable foundation for precise classification and prediction. The accuracy of the COA-ANN model was 99.6%.

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Mango leaf disease classification using hybrid Coyote-Grey Wolf optimization tuned neural network model

Cited by 3 publications

References 33 publications

Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition

Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition

Advancing mango leaf variant identification with a robust multi-layer perceptron model

A Coyote-Inspired Approach for Systemic Lupus Erythematosus Prediction Using Neural Networks

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