A scalable intelligent framework for sustainable crop management : disease prediction and automated resource allocation

Description: This research paper presents an intelligent and scalable framework for sustainable crop management, focusing on early plant disease prediction and automated resource allocation. The proposed system combines modern technologies such as machine vision, environmental sensors, IoT networks, and artificial intelligence to analyze real-time data and optimize the use of agricultural inputs including water, fertilizers, and pesticides. By leveraging a hierarchical architecture that supports both edge and cloud processing, the framework enables accurate local decision-making and global farm monitoring. The system is designed to reduce operational costs and resource wastage while increasing productivity, making it highly suitable for both small-scale and large-scale agricultural environments. This work was collaboratively developed by four undergraduate students from the Department of Computer Engineering, University of Mazandaran, Babol, Iran: Aliasghar Zahdyan Tejeneky (corresponding author) Ahmadreza Shokrollahniya Roshan Amir Mohammad Mehdizade Ganji Ali Babanezhad Malakshah Our shared goal in this project is to apply cutting-edge technologies to support the transformation of traditional agriculture into a smart, data-driven, and sustainable industry.