Developing an Adaptive Learning System for Agricultural Extension Using Painting Training-Based Optimization

Abstract

Agricultural extension services face significant challenges delivering effective training to heterogeneous farming populations with diverse knowledge levels and resource constraints. Traditional uniform training approaches result in inefficiencies where experienced farmers encounter redundant content while novice farmers struggle with excessive complexity. This research develops an adaptive learning system for agricultural extension using Painting Training-Based Optimization (PTBO), a human-inspired metaheuristic algorithm. A multi-objective optimization framework was formulated incorporating knowledge gain maximization, time efficiency, sequence validity, difficulty appropriateness, and knowledge coverage, subject to time, budget, and prerequisite and essential knowledge constraints. A quasi-experimental study with 75 wheat farmers in Irbid Governorate, Jordan (2024-2025), randomly assigned participants to PTBO-personalized (n=25), GA-personalized (n=25), and traditional-uniform (n=25) groups. PTBO demonstrated superior performance: 15.3% improvement in knowledge gain over GA (32.4 vs. 28.1 points), 29.9% faster convergence (87.3 vs. 124.6 iterations), 96.2% knowledge retention at four-week follow-up, and 80.0% practical adoption versus 69.7% for traditional methods. Novice farmers achieved normalized learning gains of 0.76 compared to 0.68 (GA) and 0.66 (traditional). The research provides a deployable framework demonstrating that metaheuristic optimization effectively addresses agricultural knowledge dissemination challenges while maintaining computational efficiency for resource-constrained contexts.