黑料网

Abstract

This paper presents an enhanced multi-criteria ideation algorithm designed to optimize rapeseed germination traits, a critical phase in crop development with significant impact on yield and quality. The algorithm integrates advanced heuristics, machine learning, and multi-objective optimization to balance conflicting factors such as germination rate, resource efficiency, and environmental stress resilience. By leveraging data-driven models and evolutionary algorithms, the system refines optimization strategies based on real-time data and historical agricultural insights. This approach offers a novel method for addressing key challenges in rapeseed cultivation, including climate adaptation, resource management, and genetic selection, ultimately improving germination success and overall crop performance. The proposed algorithm holds promise for enhancing global agricultural practices by advancing the precision and efficiency of seed germination optimization.