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Table 12 Classification performance of all classifiers by applying leave-one-observation-cross-validation techniques with selected features

From: Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves

Quality metricsWater content (%)SVMKNND-Tree
Coffee leaf
 Day 182.84   
  SENS 111
  SPEC 111
 Day 241.22   
  SENS 10.9290.976
  SPEC 0.9880.9651
 Day 312.34   
  SENS 0.9630.8891
  SPEC 10.9120.99
 Day 40.51   
  SENS 111
  SPEC 111
Peashoot
 Day 176.84   
  SENS 111
  SPEC 111
 Day 249.22   
  SENS 10.8921
  SPEC 0.9620.9820.971
 Day 318.91   
  SENS 0.5450.7270.636
  SPEC 0.9840.9670.984
 Day 40.21   
  SENS 0.9190.850.833
  SPEC 0.9870.850.933
Spinach
 Day171.14   
  SENS 0.99511
  SPEC 111
 Day234.22   
  SENS 111
  SPEC 0.97611
 Day310.34   
  SENS 0.9090.5450.851
  SPEC 0.9230.9490.897
 Day40.10   
  SENS 0.7270.8180.636
  SPEC 0.9740.8720.949