Validation of leaf area index of maize for graded levels of fertilizers using conventional and artificial intelligence techniques

Abstract

A field experiment was conducted at MARS, Dharwad during kharif, 2023-24 for validation of leaf area index of maize for graded levels of fertilizers using conventional and artificial intelligence techniques. The results showed thatapplication of 150 per cent RDF recorded significantly higher grain yield (75.64 q ha-1) and stover yield (96.18 q ha-1) ofmaize than 50 per cent RDF (38.69 q ha-1 and 55.71 q ha-1, respectively) and it was on par with 100 per cent RDF (72.77q ha-1 and 94.64 q ha-1, respectively). Among the subplots (Methods of LAI estimation) there was no significant difference. Among interactions, 150 per cent RDF + LAI estimation by artificial intelligence (AI) method showed significantly highergrain yield (75.70 q ha-1) and stover yield (96.26 q ha-1) than control. Among the different methods of LAI estimation, AImethod showed least deviation (1.02-14.77%) particularly at grain filling (1.02%) followed by silking stage (2.9%) andmaximum deviation (46.1-58.0%) was observed with disc method at all the growth stages. Among machine learning models,random forest model outperformed other models with R² (0.67-0.94) and RMSE (0.02-0.26) at all the growth stages (Kneehigh stage, tasseling stage, siliking stage and grain filling stage) compared to other models.