Deep insight : Mathematical modeling and statistical analysis for mango leaf disease classification using advanced deep learning models

Mathur, Priya; Sheth, Farhan; Goyal, Dinesh; Gupta, Amit Kumar

doi:10.47974/JIM-1830

Open Access Research Article

Deep insight : Mathematical modeling and statistical analysis for mango leaf disease classification using advanced deep learning models

Priya Mathurdrpriyamathur21@gmail.comDepartment of MathematicsPoornima Institute of Engineering & TechnologyJaipur, Rajasthan, IndiaView full profile → , Farhan Shethfarhansheth.jb@gmail.comDepartment of Computer Science & EngineeringManipal University JaipurJaipur, Rajasthan, IndiaView full profile → , Dinesh Goyaldinesh.goyal@poornima.orgDepartment of Computer Science & EngineeringPoornima Institute of Engineering & TechnologyJaipur, Rajasthan, IndiaView full profile → , ^*Amit Kumar GuptaCorresponding authordramitkumargupta1983@gmail.comDepartment of Computer Science & EngineeringManipal University JaipurJaipur, Rajasthan, IndiaView full profile →

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pp. 317–342Vol. 27Issue 2March 2024DOI: 10.47974/JIM-1830XML

Published Online:: 19 Mar 2024
Article type:: Research Article
Language:: EN
Article no.:: JIM-1830
Pages:: 317–342

Abstract

This paper presents a comprehensive investigation into the mathematical modeling and statistical analysis of classification of mango leaf diseases employing state-of-the-art Deep Learning models. The literature review underscores the significance of automated disease detection in agriculture, with Convolutional Neural Networks (CNNs) emerging as pivotal tools for image-based tasks. The proposed methodology encompasses meticulous preprocessing, optimal hyperparameter tuning, and fine-tuning of pretrained models (Inception V3, MobileNet V3 Small, MobileNet V3 Large, and ResNet50). Results indicate rapid convergence and outstanding accuracy during initial training, with all models achieving 100% accuracy on both validation and test datasets. K-Fold cross-validation affirms the models’ consistency, with Inception V3 demonstrating leading performance. Detailed analyses, including training and loss graphs and confusion matrices, offer nuanced insights and highlight areas for refinement, particularly in distinguishing Healthy leaf, Gall Midge, and Anthracnose. This research positions the proposed methodology as a promising approach with potential applications in real-world agricultural scenarios, where precise disease detection is critical for effective crop management and optimal yields.

Keywords

Mathematical modeling Statistical analysis Mango leaf diseases Convolutional neural networks (CNNs)Image-based classification Inception V3 MobileNet V3 ResNet50

Subject Classifications

68T0568T07

References

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