A Feed-Forward Neural Network–Based Framework for Integrating Special Education Programs into Agriculture and Food Systems toward Sustainable Development

Abstract

The present research examines the convergence of special education programs in agriculture and food systems to address significant concerns, including food insecurity, educational diversity, and inclusion, for sustainable development. The analysis uses data to investigate participation rates of individuals with specific food production requirements (inclusive versus non-inclusive), their satisfaction levels, and employment outcomes. Various machine learning models, including feedforward neural networks (FFNN), random forests (RF), k-nearest neighbors (KNN),  support vector regression (SVR), linear regression, and polynomial regression, were used to predict outcomes and evaluate them against expected results across multiple parameter dimensions. The findings highlight the complementary benefits of integrating agricultural education into special education programs, indicating that it helps enhance cognitive abilities, social development, and career opportunities for individuals with special needs. This study underscores the significant role of agricultural education in improving nutritional literacy, fostering independence, and promoting global food security. This data-driven approach demonstrates the potential to optimize food-system education policies through interdisciplinary solutions, thereby contributing to a more equitable and environmentally conscious future for vulnerable populations. The study advocates for a comprehensive, integrated development paradigm that meets the nutritional needs of individuals with special requirements while advancing overall food security objectives.