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Enhancing Harvest Prediction Using Artificial Intelligence Techniques

This research by Jonathan St. Clair explores the application of artificial intelligence (AI) to improve the accuracy of harvest predictions. Recognizing the challenges posed by incomplete historical data and numerous variables, the study seeks to identify and implement appropriate AI techniques that can optimize farm management for better production outcomes. Key deliverables include interim and final reports, a software design document, and a software prototype. The project aims to enable farm management to adapt effectively to changing conditions, enhancing overall planning and decision-making.

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Enhancing Harvest Prediction Using Artificial Intelligence Techniques

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  1. Supporting harvestprediction using artificial intelligence techniques Jonathan St Clair Computer Science Honours 2003 Jonathan St Clair STCJON003 jstclair@cs.uct.ac.za 10th September 2003

  2. Background • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  3. Overview • On going research done to better predict harvest figures • Often historical data is incomplete thus making prediction difficult • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  4. Complex Adaptive Systems • Too many variables for management to optimise production for both short and long term production • Not possible for management to work through every possible scenario • Seasonal variations difficult to predict • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  5. Objectives • To identify aspects which could be meaningfully enhanced by the use of AI techniques • To select the most promising opportunity within the prediction and planning of the farm and • Describe the environment and its challenges in detail. • Select the most appropriate AI technique/s and describe their application to the problem. • Illustrate how the farm management will benefit from this application of technology to the business.  • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  6. Deliverables • Interim report describing area of application for AI (I&J) • Software design document (UCT & I&J) • Final report (UCT & I&J) • Software prototype (UCT & I&J) • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  7. Impact • Enable management to quickly and reliably assess the impact of changing any of a number of variables • Increase the ability of the farm management to prepare themselves to meet a particular demand in the best possible way • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  8. Success Factors • The software must be shown to endorse or contradict decisions made using the current management system • A number of test cases, of the farmers choosing, will be constructed to allow for the farmer to make judgements in the normal fashion • The AI system will be tested on the same cases and if it is shown that the system is consistently more correct, the system will be deemed successful • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  9. Related Work • Robert M. Dorazio and Fred A. Johnson, Bayesian and Decision Theory – A Coherent Framework for Decision Making in Natural Resource Management.  • Andrew Wilson, Consumer Demand and the Future of the Supply Chain  • Anet Potgieter, “Complex Adaptive Systems, Emergence and Engineering: The Basics.”  • Anet Potgieter, “Bayesian Behaviour Networks as Hyperstructures” • Fred Johnson & Ken Williams, “Protocol and Practice in the Adaptive Management of Waterfowl Harvests”, http://www.consecol.org/vol3/iss1/art8/ • Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”      ISBN 1-55860-535-5, 37 -55, 343 – 346  • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

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