Identification of Tilletia foetida, Ustilago tritici, and Urocystis tritici Based on Near-Infrared Spectroscopy

Table 7

Pathogen identification results of the BPNN models built with different modeling ratios in each modeling spectral region when Data treatment method 2 was used.

Spectral region (cm^−1)	Modeling ratio of training set to testing set	The number of neurons in the hidden layer	Training sets		Testing sets
			Average identification accuracy for the 10 data subsets (%)	Standard deviation of the identification accuracies for the 10 data subsets	Average identification accuracy for the 10 data subsets (%)	Standard deviation of the identification accuracies for the 10 data subsets
4000–12000	3 : 1	100	99.70	0.006994	99.78	0.006660
4000–12000	4 : 1	10	98.00	0.04237	97.22	0.04648
4000–12000	5 : 1	50	98.02	0.04058	97.67	0.05176
4000–5000	3 : 1	50	92.14	0.1541	92.00	0.1508
4000–5000	4 : 1	100	96.57	0.04367	96.39	0.04488
4000–5000	5 : 1	100	92.88	0.1175	93.33	0.1183
4000–6000	3 : 1	50	99.54	0.009770	99.56	0.01332
4000–6000	4 : 1	50	99.71	0.008580	99.44	0.01668
4000–6000	5 : 1	10	99.59	0.01024	99.33	0.01332
4000–7000	3 : 1	10	98.02	0.04279	97.78	0.04662
4000–7000	4 : 1	50	95.29	0.08590	95.00	0.08766
4000–7000	5 : 1	50	99.59	0.01024	99.67	0.009990
4000–8000	3 : 1	10	99.31	0.01617	99.33	0.01017
4000–8000	4 : 1	50	100	0	100	0
4000–8000	5 : 1	50	100	0	100	0
4000–9000	3 : 1	10	99.92	0.002280	99.78	0.006660
4000–9000	4 : 1	10	99.57	0.009156	99.17	0.01780
4000–9000	5 : 1	50	99.93	0.002040	99.67	0.009990
4000–10000	3 : 1	10	99.77	0.004893	99.78	0.006660
4000–10000	4 : 1	10	99.64	0.007314	99.44	0.01668
4000–10000	5 : 1	10	99.73	0.006261	100	0
5000–6000	3 : 1	50	99.54	0.01143	99.11	0.02038
5000–6000	4 : 1	50	95.14	0.1272	94.44	0.1400
5000–6000	5 : 1	50	100	0	100	0
5000–7000	3 : 1	10	99.62	0.009181	99.33	0.01421
5000–7000	4 : 1	10	99.29	0.01153	98.89	0.01844
5000–7000	5 : 1	10	97.67	0.05708	97.33	0.05925
5000–8000	3 : 1	10	99.85	0.004590	99.56	0.008880
5000–8000	4 : 1	50	99.86	0.004290	99.44	0.01112
5000–8000	5 : 1	10	99.80	0.006150	100	0
5000–9000	3 : 1	100	99.92	0.002280	99.78	0.006660
5000–9000	4 : 1	50	99.79	0.004573	99.44	0.01668
5000–9000	5 : 1	100	99.80	0.004381	99.67	0.009990
5000–10000	3 : 1	50	100	0	100	0
5000–10000	4 : 1	50	100	0	99.72	0.008340
5000–10000	5 : 1	50	99.66	0.01026	99.67	0.009990
6000–7000	3 : 1	50	94.28	0.1015	94.22	0.1010
6000–7000	4 : 1	50	95.72	0.08484	94.72	0.08094
6000–7000	5 : 1	50	88.77	0.1922	87.67	0.1700
6000–8000	3 : 1	50	100	0	99.78	0.006660
6000–8000	4 : 1	50	99.86	0.004290	99.44	0.01112
6000–8000	5 : 1	50	97.88	0.04379	97.00	0.06046
6000–9000	3 : 1	100	98.24	0.04069	98.00	0.04604
6000–9000	4 : 1	50	99.86	0.002840	99.44	0.01112
6000–9000	5 : 1	50	99.93	0.002040	99.67	0.009990
6000–10000	3 : 1	100	99.47	0.01186	99.33	0.01421
6000–10000	4 : 1	50	97.93	0.04233	96.67	0.04779
6000–10000	5 : 1	10	99.04	0.02435	99.33	0.01332
7000–8000	3 : 1	50	99.85	0.003040	99.33	0.01421
7000–8000	4 : 1	50	99.86	0.004290	99.44	0.01668
7000–8000	5 : 1	50	99.93	0.002040	99.00	0.02135
7000–9000	3 : 1	50	98.24	0.04040	97.33	0.04534
7000–9000	4 : 1	50	98.43	0.03805	97.22	0.04648
7000–9000	5 : 1	50	97.95	0.04029	97.33	0.05122
7000–10000	3 : 1	50	95.65	0.08542	95.56	0.08374
7000–10000	4 : 1	50	99.29	0.01356	98.33	0.02833
7000–10000	5 : 1	50	99.66	0.006296	99.33	0.01332
8000–9000	3 : 1	50	97.79	0.04002	97.11	0.03854
8000–9000	4 : 1	50	98.00	0.03949	96.39	0.05423
8000–9000	5 : 1	50	97.74	0.04030	96.67	0.04945
8000–10000	3 : 1	10	94.12	0.1173	93.56	0.1207
8000–10000	4 : 1	10	96.14	0.06422	95.28	0.05700
8000–10000	5 : 1	10	97.13	0.04351	96.67	0.05579
9000–10000	3 : 1	50	93.89	0.08533	93.56	0.08401
9000–10000	4 : 1	50	97.36	0.05689	96.11	0.07049
9000–10000	5 : 1	50	97.74	0.04076	97.00	0.05261