Hybrid deep learning approach for cotton leaf disease detection and management using fine-tuned VGG16 and Inception v3 models

Abstract

Agricultural productivity is frequently threatened by crop diseases, leading to substantial economic losses and hindering the adoption of sustainable farming practices. Early detection and timely intervention are therefore critical to mitigate these risks. This paper presents a novel hybrid deep learning method (HDLM) for accurate classification of cotton leaf diseases by integrating the strengths of two fine-tuned deep learning models-Visual Geometry Group 16 and Inception v3-through a stacking ensemble strategy that combines their predictions at the output level. The model was trained and validated on a carefully curated dataset of 3,000 images, manually labeled into six classes: Aphids, Armyworm, Bacterial Blight, Powdery Mildew, Target Spot, and Healthy Leaves, with 2,400 images used for training and 600 reserved for validation. To enhance generalization and robustness against real-world variations in leaf orientation, illumination, and background, extensive data augmentation techniques were applied, including rotations, flips, zooming, translations, and brightness adjustments. The proposed HDLM achieved a classification accuracy of 98.56%, significantly outperforming benchmark models such as AlexNet, DenseNet-121, ResNet-50, LeNet-5, and a 7-layer convolutional neural network, which achieved accuracies of 90–95%. In addition, the model incorporates a disease management recommendation system that provides actionable guidance to farmers to mitigate diseases and improve crop yields. This research demonstrates the efficacy of ensemble deep learning techniques in plant disease detection, providing a scalable, robust, and practical solution for precision agriculture. Future work should focus on expanding the dataset with heterogeneous sources, integrating advanced augmentation strategies, and exploring real-time feedback mechanisms to further enhance model adaptability, predictive performance, and applicability across diverse agricultural environments.

Keywords

Cotton leaf disease detection / Hybrid deep learning / Visual Geometry Group 16 / Inception v3 / Ensemble learning

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