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Machine learning and deep learning models integration: Detection of apple leaves diseases

Anupam Bonkra, Sunil Pathak, Amandeep Kaur

Abstract


Differentiating between sick states is made possible by variations in the visual properties of leaf diameters, which makes leaves useful markers for disease diagnosis. Recognizing the unique patterns that infections leave on foliage is crucial to correctly diagnosing diseases. Plant inspection has often been carried out by specialists or cultivators, which may be expensive and time-consuming. Thus, illness detection automation is essential, particularly in places where access to experts is restricted. In order to create a model for identifying illnesses on apple leaves, this study uses five classification algorithms: Inception V3, Support Vector Machine (SVM), Random Forest, and Decision Tree. Apple Scab, Apple Spot, and Apple Rust are the main subjects of the study. To detect these illnesses, a comparative examination of machine learning and deep learning models is carried out using the "Apple Leaves Disease Dataset." Among all the models tested, VGG19 achieved the highest test accuracy, reaching an impressive 95 percent.


Keywords


apple; leaf; disease; classification; deep learning; machine learning; scab; spot; rust

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References


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DOI: https://doi.org/10.32629/jai.v7i5.1756

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