DATA-DRIVEN APPROACHES TO EARLY PLANT DISEASE DIAGNOSIS USING AI AND ROBOTICS

Abstract:This scientific article examines the current state of data analysis applications for early detection of plant diseases, the existing challenges, and effective strategies to address them. The study is based on an analytical review of scientific literature, statistical reports, international case studies, and practical initiatives in smart agriculture. It highlights the key advantages of artificial intelligence and data-driven approaches in plant disease detection, while also identifying major barriers such as insufficient data quality, limited digital infrastructure, and a shortage of skilled specialists. Furthermore, strategic solutions are proposed to improve system efficiency, including expanding agricultural image databases, optimizing machine learning and deep learning algorithms, and strengthening collaboration between researchers, technology developers, and agricultural stakeholders. The results demonstrate that data analysis plays a vital role in ensuring plant health monitoring and emphasize the necessity of a comprehensive and integrated approach for large-scale adoption of intelligent plant disease detection systems.