Table 4.

Root mean squared error (%) between observed and predicted values from the 10 best artificial neural networks selected and multiple linear regression (MLR) for the for predicting the variables leaf nitrogen (LNC), ear insertion height (EIH), total height (PH), grain yield (GY) in maize using the SAVI and GNDVI vegetation indices as input

Topology*	LNC		EIH		PH		GY
	T	V	T	V	T	V	T	V
2–8	18.92	18.36	42.10	41.03	27.19	26.56	43.46	42.17
4–10	18.15	18.13	40.15	39.99	26.99	26.19	43.18	42.99
5–10	17.95	17.76	39.95	39.57	25.68	25.60	42.98	42.98
6–6	17.45	17.43	39.10	38.17	24.36	23.44	42.67	42.15
8–8	17.03	16.96	38.52	37.10	23.79	22.11	42.01	41.15
2–4-8	16.15	14.15	37.35	36.50	22.31	20.19	41.40	40.45
2–6-8	15.89	15.02	36.55	36.17	21.27	21.03	40.15	38.99
2–10-10	15.00	14.93	35.17	34.94	20.45	20.02	39.19	38.93
4–4-8	14.97	14.12	34.46	33.33	19.50	18.91	38.98	38.10
4–8-2	14.28	13.11	33.21	31.99	18.19	17.50	37.45	36.12
MLR	25.24	23.29	56.01	52.15	39.15	36.18	70.89	70.01

^*the values between the lines refer to the number of neurons in each layer; T: training (80% of the data); V: validation (20% of the data)