Thesis Abstract

Crop diseases pose a significant threat to global food production and agricultural sustainability. This study aims to explore the application of artificial intelligence (AI) techniques in crop disease detection and management to enhance disease control strategies and improve crop health. The research will focus on developing AI-based models and algorithms that can accurately identify and classify crop diseases using various data sources, such as images, sensor data, and spectral signatures. Machine learning and deep learning techniques will be employed to train and optimize these models using large datasets of diseased and healthy crop samples. Additionally, the study will investigate the integration of AI with precision agriculture technologies, such as remote sensing and Internet of Things (IoT), to enable real-time disease monitoring and early detection. The outcomes of this research will provide valuable insights into the development of AI-driven decision support systems for crop disease management, enabling farmers to make timely and informed decisions regarding disease control measures, including targeted pesticide application and disease-resistant crop selection. By leveraging the power of AI, we can enhance disease detection accuracy, reduce crop losses, minimize pesticide use, and promote sustainable agricultural practices. Ultimately, the application of AI in crop disease detection and management has the potential to revolutionize the way we protect and sustainably manage our agricultural resources.

Thesis Overview