Enhanced Plant Disease Detection System with Multimodel Deep Learning and CGAN Augmentation
Keywords:
Adversarial networks, Conditional generative, Convolutional neural networks, Deep learning, Plant disease detectionAbstract
Crop infections significantly hinder agricultural output, resulting in major financial setbacks and endangering global food supply. This issue is especially critical in underdeveloped areas, where modern disease detection technologies are often unavailable. Early identification of plant diseases is essential for prompt action, minimizing crop damage, and boosting agricultural productivity. This study introduces an advanced disease recognition system for plants, combining Convolutional Neural Networks (CNNs), conditional Generative Adversarial Networks (cGANs), and on-device AI implementation using the Seeed ESP32 platform along with Grove Vision AI. The proposed framework utilizes advanced deep learning methods for precise identification of plant diseases and incorporates cGANs to produce high-fidelity synthetic images. This strategy helps to correct dataset imbalances, enhances the model’s ability to generalize, and improves detection accuracy, especially for rare or less-represented diseases. The study emphasizes the effectiveness of combining deep learning, synthetic image augmentation, and Edge AI to develop scalable and affordable solutions for managing plant health.
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