Toward Smart Agriculture: AI-Optimized Prototype Conceptual Design for Lentil Seed Germination with UV-C and Spirulina Academic Article in Scopus uri icon

abstract

  • This study introduces an adaptable, intelligent prototype designed to optimize lentil seed germination and biomass accumulation via controlled UV-C radiation and Spirulina supplementation. Building on earlier experiments that separately and jointly assessed these treatments, the work presents a novel seed-treatment chamber that combines environmental sensing, real-time delivery mechanisms, and a machine-learning decision engine. The system automatically selects among three operational modes, Fast Germination, High Biomass, and Flavonoid Enrichment, each targeting a specific agronomic goal. To uncover the most influential treatment factors, the authors applied Analysis of Variance (ANOVA) and Principal Component Analysis (PCA), revealing key response patterns that inform mode definitions. A regression-based AI model was then trained on experimental data to predict treatment outcomes and dynamically adjust parameters. Model performance metrics demonstrate high predictive fidelity, with a Mean Absolute Error (MAE) of 2.1267%, indicating an average deviation of just over two percentage points between predicted and observed germination rates. In comparison, a Mean Squared Error (MSE) of 6.4598 and a corresponding Root Mean Squared Error (RMSE) of 2.5416% confirm consistently low squared deviations. An R2 score of 0.8702 indicates that the model accounts for approximately 87% of the variance in germination outcomes, underscoring the robustness of the regression approach. Importantly, the specific treatment ranges illustrated in this study are not direct replications of prior data, but rather representative values drawn from earlier research to demonstrate the framework¿s applicability. By abstracting treatment parameters into realistic ranges, the paper shows how the chamber can accommodate various empirical datasets. The principal contribution lies in offering a generalizable methodology for designing AI-enhanced seed-treatment systems. This conceptual framework can be tailored to multiple crops and cultivation environments, paving the way for scalable, precision agriculture solutions that integrate automated monitoring, intelligent control, and real-time optimization. © 2025 by the authors.

publication date

  • December 1, 2025