Stacked Ensembles Powering Smart Farming for Imbalanced Sugarcane Disease Detection

Sugarcane is a vital crop, accounting for approximately 75% of the global sugar production. Ensuring its health through the early detection and classification of diseases is essential in maximizing crop yields and productivity. While recent deep learning advancements, such as Vision Transformers, have shown promise in sugarcane disease classification, these methods often rely on resource-intensive models, limiting their practical applicability. This study introduces a novel stacking-based ensemble framework that combines embeddings from multiple state-of-the-art deep learning methods. It offers a lightweight and accurate approach for sugarcane disease classification. Leveraging the publicly available sugarcane leaf dataset, which includes 7134 high-resolution images across 11 classes (nine diseases, healthy leaves, and dried leaves), the proposed framework integrates embeddings from InceptionV3, SqueezeNet, and DeepLoc models with stacked ensemble classifiers. This approach addresses the challenges posed by imbalanced datasets and significantly enhances the classification performance. In binary classification, the model accuracy is 98.89% and the weighted F1-score is 98.92%, while the multi-classification approach attains accuracy of 95.64% and a weighted F1-score of 95.62%. The stacking-based framework is superior to Transformer models, reducing the training time by 75% and demonstrating superior generalization across diverse and imbalanced classes. These findings directly contribute to the sustainability goals of zero hunger and responsible consumption and production by improving agricultural productivity and promoting resource-efficient farming practices.