Combining Multiple Classifiers Using Hybrid Votes Technique with Leaf Vein Angle, CNN and Gabor Features for Plant Recognition

Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive comprehension of the form and function of veins necessitates a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning (ML) have facilitated the creation of algorithms that can identify vein network and explore their developmental progressions. We reviewed the functional, environmental, and genetic factors associated with vein networks, along with the current research status based on image analysis. Additionally, the methods of venous phenotype extraction and multi-omics association analysis using ML technology are discussed, which can provide a theoretical basis for improving crop productivity by optimizing the vein network architecture.

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Enhancing Tomato Leaf Disease Detection Through Multimodal Feature Fusion

SARAF,

PATIL,

WAGH

2024

Appl. Comput. Sci.

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The need for an ensemble classifier arises due to better accuracy; reduced overfitting, increased robustness which handles the noisy data and reduced variance of individual models, by combining the advantages and overcoming the drawbacks of the individual classifier. We have performed a comparison of different classifiers like Support Vector Machine (SVM), XGBoost, Random Forest (RF), Naive Bayes (NB), Convolutional Neural Network (CNN) and proposed Ensemble method used in the classification task. Among all the classifiers evaluated, CNN was found to be the most accurate having an accuracy rate of 93.7%. This indicates that CNN can identify complex data patterns that are also important for photo recognition and classification tasks. Nonetheless, NB and SVM only achieved medium results with accuracy rates of 82.66% and 85.6% respectively. These could have been due to either the complexity of data being handled or underlying assumptions made. RF and XGBoost demonstrated remarkable performances by employing ensemble learning methods as well as gradient-boosting approaches with accuracies of 83.33% and 90.7% respectively. Our Ensemble method outstripped all individual models at an accuracy level of 95.5%, indicating that more than one technique is better when classifying correctly based on various resource allocations across techniques employed thereby improving such outcomes altogether by combining them. These results display the pros and cons of every classifier on the Plant Village dataset, giving vital data to improve plant disease classification and guide further research into precision farming and agricultural diagnostics.

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Combining Multiple Classifiers Using Hybrid Votes Technique with Leaf Vein Angle, CNN and Gabor Features for Plant Recognition

Cited by 3 publications

References 19 publications

Leaf recognition using convolutional neural networks based features

Leaf recognition using convolutional neural networks based features

Machine learning for image-based multi-omics analysis of leaf veins

Enhancing Tomato Leaf Disease Detection Through Multimodal Feature Fusion

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