Vol. 9, Issue 3, Part E (2025)

Plant diseases cause significant losses in agricultural output, with yearly crop losses amounting to 40% of overall production. Plant disease detection techniques that rely on labor-intensive processes and human skill are not up to par with large-scale agricultural operations. The study examines how drone systems combined with artificial intelligence (AI) can be used as cutting-edge plant disease detection tools. Combinations of deep learning algorithms, machine learning algorithms, and drone-mounted imaging systems allow for more effective and early detection of plant diseases. Techniques like Convolutional Neural Networks (CNNs) in conjunction with multispectral and hyperspectral imaging and edge computing for focused disease therapies have improved real-time plant health monitoring. Drones provide stakeholders with wide-ranging aerial views through aerial assessment technology, utilizing sensors and high-definition imaging systems to facilitate effective disease management strategies. The lack of sufficient labeled datasets, expensive obstacles, complicated symptoms, and ongoing algorithm updates make the widespread adoption of this technique difficult. By enhancing sustainable crop quality, these technologies have the potential to revolutionize agricultural methods and provide better solutions for global food security. For plant disease diagnostics using AI and drone technology to be as efficient and accessible as possible, ongoing research and affordable solutions are now crucial.