Hybrid Deep Learning Architectures for Automated Mango Leaf Disease Detection

Ranu Solanki; Deepak Kumar Yadav

doi:10.36948/ijfmr.2026.v08i04.84148

Hybrid Deep Learning Architectures for Automated Mango Leaf Disease Detection

Author(s)	Ms. Ranu Solanki, Dr. Deepak Kumar Yadav
Country	India
Abstract	India is one of the biggest producers and exporters of mangoes in the world, yet its cultivation is persistently threatened diseases that reduce yield, fruit quality, and orchard longevity. Traditional disease diagnosis is based on agronomists' hand visual inspection, which is a laborious, subjective, and challenging technique to scale across vast plantations. This research provides a hybrid deep learning system that incorporates AlexNet and ResNet-50 for the automated classification of five commercially relevant mango leaf diseases: Bacterial Canker, Anthracnose, Powdery Mildew, Sooty Mould and Healthy foliage. Through a fused, jointly trained classification head, the suggested architecture combines the deep, residual feature hierarchies of ResNet-50 with the shallow, texture-sensitive representations learned by AlexNet, enabling the network to take advantage of complementary visual cues that neither backbone fully captures on its own. The hybrid model was implemented and trained using MATLAB. The trained model achieved a validation accuracy of 99.47%. Comparative analysis against standalone AlexNet, standalone ResNet-50, and other architectures reported in the recent mango plant-disease literature indicates that the hybrid fusion strategy offers a favourable balance of accuracy and convergence stability.
Keywords	Mango Leaf Disease; Hybrid Deep Learning; AlexNet; ResNet-50; Convolutional Neural Network; Feature Fusion; Precision Agriculture; Image Classification
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-21
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.84148

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Hybrid Deep Learning Architectures for Automated Mango Leaf Disease Detection

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