علم الرواسب والتسلسل الزمني الجيولوجي لتسلسلات الصخور الرسوبية في العصر الأركي الوسيط في منطقة سينغبوم بالهند وتداعياتها

BFP/URG/EBR/24/008

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Automated disease detection in Omani fruit trees

Abstract

Our project aims to revolutionize disease detection in Omani fruit trees through the implementation of machine learning-based image processing algorithms.Through the utilization of artificial intelligence, our goal is to optimize the detection procedure, making it more accessible to farmers of all levels, thereby reducing the likelihood of ecological crises and bolstering food security. Drawing from existing literature, we have identified successful methodologies in disease detection across various agricultural contexts, achieving high accuracy rates. Our study suggests an all-encompassing methodology that includes image capturing, dataset generation, machine learning-driven analysis, and the creation of an intuitive decision-making tool. Anticipated outcomes include the creation of a fully automated AI system adaptable to diverse hardware, enabling efficient monitoring of crop health. By accumulating extensive data on plant illnesses nationwide, we seek to enhance our understanding of ecological dynamics, ultimately facilitating more informed agricultural practices. In summary, our project endeavors to harness the power of artificial intelligence to address critical challenges in agriculture, offering a scalable solution for disease detection and management in Omani fruit trees.