Abstract

The advancement of Artificial Intelligence (AI) allows farmers to detect diseases with great precision using innovative management tools in agricultural practices. This study examines the effectiveness of traditional approaches alongside an innovative AI-based method for managing Botrytis fabae (Sard) chocolate-spotted disease in faba beans. Experimental data confirmed that fungicides significantly reduced fungal growth, proving superior to biological agents and antioxidants for fungal treatment. During greenhouse testing, each therapy, including antioxidants, bioagents, fungicides, and the AI model, successfully reduced disease severity. Chitinase activity reached its highest levels through the use of biological agents and chemical inducers, particularly when Bio-Zeid, KHCO₃, and Plant Safeguard were applied. The AI-based prediction model outperformed traditional fungicide applications in field conditions, delivering 63% fewer disease symptoms, 8% higher yields, 12% more cost savings, and requiring 35% less fungicide consumption. This research demonstrates how AI advances farming disease management practices to promote better crop development while minimizing resource usage and disease suppression in agricultural systems. When combined with artificial intelligence, the method offers enduring sustainability over traditional practices, reducing both environmental impact and improving agricultural outcomes.

Key words: Artificial Intelligence; Computer Science; AI in Agriculture; Faba Bean; Biological Agents; Chemical Inducers; AI-based Prediction Model; Fungicides; Disease Management; Machine Learning; Agricultural Systems.