text: import cv2
  import os
  
  #Cascade
  cascade = cv2.CascadeClassifier('./golden_pothos_cascade.xml')
  
  #Reading Image
  raw_img = cv2.imread(r'./Golden Pothos/3.jpg')
  
  #Percent of original size
  scale_percent = 60 
  width = int(raw_img.shape[1] * scale_percent / 100)
  height = int(raw_img.shape[0] * scale_percent / 100)
  dimensions = (width, height)
  
  #Resizing Image
  img = cv2.resize(raw_img, dimensions, interpolation = cv2.INTER_AREA)
  
  #Converting to Gray Image
  gray_Image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
  
  #Adding Gaussian Blur
  blur=cv2.GaussianBlur(gray_Image,(13,13),cv2.BORDER_DEFAULT)
  
  #Detecting Plant
  detection_result, rejectLevels, levelWeights =cascade.detectMultiScale3(blur, scaleFactor=1.0485258, minNeighbors=6, minSize=(30,30),outputRejectLevels = 1)
  
  greaterweightindex = 0
  currentweight = levelWeights[0]
  
  #Area with Heighest Confidence
  for (weight) in levelWeights:
      if weight > currentweight:
          greaterweightindex = greaterweightindex+1
          currentweight = weight
  
  #Highest Confidence Area 
  x = detection_result[greaterweightindex][0]
  y = detection_result[greaterweightindex][1]
  w = detection_result[greaterweightindex][2]
  h = detection_result[greaterweightindex][3]
  
  #Modifying Cofidence
  confidence= round(currentweight[0], 2)
  finalconfidence= confidence * 100
  
  #Drawing Rectangle
  cv2.rectangle(img,(x,y), (x+w, y+h), (0,0,255), thickness=2)
  cv2.rectangle(img,(x,y-35), (x+w, y), (0,0,255), thickness=-1) 
  
  #Adding Text
  cv2.putText(img, str(f"Golden Pothos {finalconfidence}%"), (x,y-5), cv2.FONT_HERSHEY_COMPLEX, 0.6, (255,255,255), thickness=2)
  
  #Displaying Image
  cv2.imshow("Detected Plant",img)
  
  #Adding Wait
  cv2.waitKey(0)