AgriQ: An AI-Driven and Quantum-Optimized System for Smart Crop Planning
Supervisor Name
Emad Natsheh
Supervisor Email
e.natsheh@najah.edu
University
An-Najah National University
Research field
Artificial Intelligence
Bio
Dr. Emad Natsheh is an Associate Professor in the Faculty of Engineering and Information Technology at An-Najah National University. He holds a PhD in Artificial Intelligence and Renewable Energy and an MSc in Computer and Network Technology from Manchester Metropolitan University (UK), and a BSc in Computer Engineering from An-Najah National University. His research focuses on artificial intelligence, machine learning, smart systems, and renewable energy applications. Dr. Natsheh has published several papers in international journals and conferences and has experience in training in data science, Python programming, and data analysis.
Description
Agricultural production often suffers from a mismatch between crop supply and market demand, leading to frequent overproduction, price volatility, and financial losses for farmers. This problem is particularly evident in the Palestinian agricultural sector, where many smallholder farmers make planting decisions based on previous seasons rather than data-driven forecasts. As a result, sudden surpluses of certain crops can cause market saturation and severe price drops, negatively affecting farmer income and contributing to food waste. These challenges highlight the need for intelligent decision-support systems that can combine agronomic data with market forecasting to guide more informed crop planning. This project aims to develop AgriQ, an artificial intelligence and quantum-enhanced decision-support system designed to improve crop planning and reduce agricultural overproduction. The system will integrate machine learning models and quantum-inspired optimization techniques to recommend optimal crop allocations based on soil conditions and predicted market demand. The proposed framework consists of three main components. First, a Random Forest model will analyze soil properties and environmental factors to determine suitable crops for a given area. Second, a Long Short-Term Memory (LSTM) neural network will forecast future crop demand using historical market data. Finally, a quantum-inspired optimization algorithm will determine an optimal crop allocation strategy that balances predicted demand with agricultural production capacity. The project will be implemented with the participation of two undergraduate students, providing them with hands-on experience in artificial intelligence, machine learning, quantum-inspired optimization, and data-driven decision-support systems.
