Added PlantTech Diagnostics ML Project

ML Projects/PlantTech-Diagnostics/Leaf_analyzer.py

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+import numpy as np
+import pickle
+from PIL import Image
+import os
+import warnings
+
+warnings.filterwarnings('ignore')
+
+
+class SimpleFeatureExtractor:
+    def extract_basic_features(self, image_path):
+        """Extract simple color and texture features"""
+        img = Image.open(image_path)
+        img = img.resize((64, 64))
+        img_array = np.array(img)
+
+        if len(img_array.shape) != 3:
+            return None
+
+        features = []
+
+        # Color averages (RGB)
+        features.extend([
+            np.mean(img_array[:, :, 0]),  # Red
+            np.mean(img_array[:, :, 1]),  # Green
+            np.mean(img_array[:, :, 2])  # Blue
+        ])
+
+        # Color variations
+        features.extend([
+            np.std(img_array[:, :, 0]),
+            np.std(img_array[:, :, 1]),
+            np.std(img_array[:, :, 2])
+        ])
+
+        # Basic texture
+        gray = np.mean(img_array, axis=2)
+        h_diff = np.mean(np.abs(np.diff(gray, axis=1)))
+        v_diff = np.mean(np.abs(np.diff(gray, axis=0)))
+        features.extend([h_diff, v_diff])
+
+        return np.array(features)
+
+
+class LeafTester:
+    def __init__(self, model_file="plant_disease_model.pkl"):
+        """Load trained model"""
+        if not os.path.exists(model_file):
+            print(f" Model file '{model_file}' not found!")
+            print("Please run train_model.py first to create the model.")
+            return
+
+        # Load saved model
+        with open(model_file, 'rb') as f:
+            model_data = pickle.load(f)
+
+        self.model = model_data['model']
+        self.label_encoder = model_data['label_encoder']
+        self.extractor = SimpleFeatureExtractor()
+
+        print(f" Model loaded from: {model_file}")
+        print(f" Can detect: {list(self.label_encoder.classes_)}")
+
+    def predict_leaf(self, image_path):
+        """Predict disease for single leaf photo"""
+
+        if not os.path.exists(image_path):
+            print(f" Photo not found: {image_path}")
+            return None, 0
+
+        print(f"\n Analyzing photo: {image_path}")
+
+        try:
+            # Extract features
+            features = self.extractor.extract_basic_features(image_path)
+
+            if features is None:
+                print(" Could not process image (invalid format?)")
+                return None, 0
+
+            # Predict
+            prediction = self.model.predict([features])[0]
+            probabilities = self.model.predict_proba([features])[0]
+            confidence = np.max(probabilities)
+
+            # Decode result
+            disease_name = self.label_encoder.inverse_transform([prediction])[0]
+
+            # Display results
+            print(f" DIAGNOSIS: {disease_name}")
+            print(f" CONFIDENCE: {confidence:.3f} ({confidence * 100:.1f}%)")
+
+            # Interpretation
+            if confidence > 0.8:
+                print(" HIGH CONFIDENCE - Trust this result")
+            elif confidence > 0.6:
+                print("  MEDIUM CONFIDENCE - Probably correct")
+
+
+            return disease_name, confidence
+
+        except Exception as e:
+            print(f" Error processing image: {str(e)}")
+            return None, 0
+
+
+# MAIN TESTING SCRIPT
+if __name__ == "__main__":
+
+
+    # Load trained model
+    tester = LeafTester("plant_disease_model.pkl")
+
+    if hasattr(tester, 'model'):
+        print(f" READY TO TEST LEAF PHOTOS!")
+
+
+        # TEST YOUR PHOTOS HERE
+        test_photos = [
+            "healthy_leaf.jpg",
+            "Suspicious_leaf.JPG"
+        ]
+
+        for photo_path in test_photos:
+            if os.path.exists(photo_path):
+                disease, confidence = tester.predict_leaf(photo_path)
+
+            else:
+                print(f"  Skipping: {photo_path} (file not found)")

ML Projects/PlantTech-Diagnostics/algorithm_comparison_results.csv

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+Algorithm,Accuracy,Precision,Recall,F1-Score,Training_Time,Prediction_Time
+Random Forest,0.625,0.6467629490392649,0.625,0.6290784807876036,0.3490755558013916,0.0053119659423828125
+SVM (RBF Kernel),0.635,0.6497204360826652,0.635,0.6378240695746417,0.07944583892822266,0.009997129440307617
+K-Nearest Neighbors,0.63,0.6464991373360938,0.63,0.6330341571901571,0.003178119659423828,0.006959676742553711