Edge-Deployable Lightweight CNN Ensembles for Real-Time Soursop Disease Detection in Smart Farming

Abstract

The accurate and early detection of plant diseases is critical for ensuring agricultural productivity and sustainability, particularly for medicinal plants like Soursop (Annona muricata), which holds significant therapeutic value. Traditional methods for disease identification are often labor-intensive, subjective, and inaccessible to farmers in remote areas. To address these challenges, this study proposes an edge-deployable lightweight ensemble model combining multiple Convolutional Neural Network (CNN) architectures for realtime Soursop leaf disease detection. A comprehensive methodology was adopted, involving data collection, preprocessing, model training, ensemble fusion, and TensorFlow Lite (TFLite) optimization for mobile deployment. Four state-of-theart CNN models—VGG19, ResNet101, InceptionV3, and DenseNet201—were individually trained and evaluated. The experimental results demonstrate that VGG19, InceptionV3, and DenseNet201 achieved outstanding validation accuracies of 99.84%–100%, with high AUC scores of 1.00, indicating excellent model performance. In contrast, ResNet101 underperformed, achieving only 32.90% validation accuracy despite a high AUC score, highlighting issues of overfitting or dataset mismatch. The ensemble model, developed through a softvoting strategy, achieved a perfect 100% validation and testing accuracy, with an AUC score of 1.00, outperforming all individual models. This lightweight ensemble approach ensures high classification performance while maintaining low computational complexity, making it suitable for real-time applications in smart farming environments. The final system is optimized for mobile devices, enabling farmers to perform offline disease detection efficiently without dependency on cloud services. Overall, the study demonstrates a robust, scalable, and sustainable solution for advancing precision agriculture, empowering farmers with accessible AI-driven tools to improve crop health monitoring and preserve valuable medicinal plant resources.