diff --git a/Image.py b/Image.py
index a2adfe5..b4fb11b 100644
--- a/Image.py
+++ b/Image.py
@@ -55,15 +55,25 @@ class Image:
     
     def return_gasStation(self):
         return self.gasStation_image
-    
+
+# losowanie zdjęcia z testowego datasetu bez powtórzeń
+imagePathList = []
 def getRandomImageFromDataBase():
     label = random.choice(neuralnetwork.labels)
-
     folderPath = f"dataset/test/{label}"
     files = os.listdir(folderPath)
     random_image = random.choice(files)
     imgPath = os.path.join(folderPath, random_image)
 
+    while imgPath in imagePathList:
+        label = random.choice(neuralnetwork.labels)
+        folderPath = f"dataset/test/{label}"
+        files = os.listdir(folderPath)
+        random_image = random.choice(files)
+        imgPath = os.path.join(folderPath, random_image)
+
+    imagePathList.append(imgPath)
+
     image = pygame.image.load(imgPath)
     image=pygame.transform.scale(image,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
     return image, label, imgPath
diff --git a/Tractor.py b/Tractor.py
index 4a5d10e..1312092 100644
--- a/Tractor.py
+++ b/Tractor.py
@@ -198,7 +198,7 @@ class Tractor:
             for j in range(initPos[0], dCon.NUM_X):
                 if self.slot.imagePath != None:
                     predictedLabel = nn.predictLabel(self.slot.imagePath, model)
-                    print(str("Coords: ({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis), "real: ", self.slot.label, "predicted: ", predictedLabel, "correct" if (self.slot.label == predictedLabel) else "incorrect")
+                    print(str("Coords: ({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis), "real:", self.slot.label, "predicted:", predictedLabel, "correct" if (self.slot.label == predictedLabel) else "incorrect", 'nawożę za pomocą:', nn.fertilizer[predictedLabel])
                 self.move_forward(pole, False)
             if i % 2 == 0 and i != dCon.NUM_Y - 1:
                 self.turn_right()
diff --git a/neuralnetwork.py b/neuralnetwork.py
index aa21ffa..5e424e0 100644
--- a/neuralnetwork.py
+++ b/neuralnetwork.py
@@ -9,8 +9,8 @@ from PIL import Image
 import random
 
 imageSize = (128, 128)
-labels = ['beetroot', 'potato', 'carrot']
-labels.sort()
+labels = ['beetroot', 'carrot', 'potato'] # musi być w kolejności alfabetycznej
+fertilizer = {labels[0]: 'kompost', labels[1]: 'saletra amonowa', labels[2]: 'superfosfat'}
 
 torch.manual_seed(42)