Leveraging artificial intelligence and big data to advance phytoplasma disease detection and crop health management

Phytoplasmas are minute, cell wall-less bacteria that infect various economically important crops worldwide, leading to significant agricultural losses. Traditional diagnostic methods are often time-consuming, require specialized expertise, and thus limit timely intervention. This study addressed these challenges by combining two complementary approaches. The first is devising an artificial intelligence (AI)-based diagnostic system utilizing Convolutional Neural Networks (CNNs) trained on extensive image datasets, enabling rapid, accurate detection of phytoplasma infections with promising accuracy. The second approach involves the construction of a comprehensive image and symptom database that allows farmers to compare crop symptoms for early disease identification. The image database serves as an initial online-accessible tool for proactive crop management while the AI model is being further optimized. Eventually, the AI diagnostic model and image database will be integrated, forming a scalable, powerful solution aimed at advancing phytoplasma disease management and enhancing crop protection strategies.