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Table 1 Relationship between the number of training images and the performance of flowering detection

From: Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images

Training number 5 15 30 50 100 300
Accuracy(+) 0.74± 0.05 0.81± 0.04 0.83± 0.03 0.79± 0.03 0.73± 0.02 0.64
TP rate(+) 0.65± 0.13 0.61± 0.12 0.59± 0.09 0.49± 0.08 0.31± 0.04 0.09
TN rate(+) 0.8 ± 0.09 0.95± 0.03 0.99± 0.00 0.99± 0.00 1 ± 0.00 1.0
  1. +Accuracy, TP rate, and TN rate, were defined as follows:
  2. \( \mathrm{Accuracy}=\frac{\mathrm{TP} + \mathrm{T}\mathrm{N}}{\mathrm{TP} + \mathrm{F}\mathrm{P} + \mathrm{T}\mathrm{N} + \mathrm{F}\mathrm{N}},\mathrm{T}\mathrm{P}\mathrm{rate}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{F}\mathrm{N}},\mathrm{T}\mathrm{N}\ \mathrm{rate}=\frac{\mathrm{TP}}{\mathrm{FP}+\mathrm{T}\mathrm{N}} \)
  3. where TP, TN, FP, and FN represent the numbers of true positives, true negatives, false positives, and false negatives, respectively, of the confusion matrix.