An Enhanced Dynamic Autoencoder Based on Gaussian Mixture Model for Plant Disease Detection

ABSTRACT Agriculture plays a fundamental role in the global economy, contributing about 6% to global gross domestic product, while in many low‐income nations, it accounts for up to 25%–40% of gross domestic product. Plants provide around 75% of the food consumed by humans and are the primary nutritional source for animals, making plant health crucial to global food security. However, plant diseases represent a serious threat to agricultural productivity, causing 10%–30% loss in global crop yields annually. The early detection and diagnosis of such diseases are thus vital to sustaining agricultural productivity. While deep learning techniques—particularly deep clustering—have shown promise in image‐based disease detection, they often face limitations when dealing with complex and multi‐modal data. To overcome these limitations, we propose an enhanced Dynamic Autoencoder method based on a Gaussian Mixture Model for plant disease detection. Unlike conventional methods that rely heavily on labeled data, our approach leverages probabilistic clustering and adaptive feature learning to distinguish between healthy and diseased plants more effectively. We evaluate our method using the PlantVillage dataset and demonstrate that our approach outperforms traditional deep learning and clustering approaches. The experimental results highlight the model's superior accuracy and normalized mutual information, underscoring its potential for scalable and intelligent plant disease monitoring in smart agriculture.