Added Tomato Leaf Disease Dataset

agml/_assets/public_datasources.json

@@ -1338,6 +1338,35 @@
             ]
         }
     },
+    "tomato_leaf_disease": {
+        "classes": {
+            "1": "Bacterial Spot",
+            "2": "Early Blight",
+            "3": "Healthy",
+            "4": "Late Blight",
+            "5": "Leaf Mold",
+            "6": "Septoria Leaf Spot",
+            "7": "Spider Mites Two-spotted Spider Mite",
+            "8": "Target Spot",
+            "9": "Tomato Mosaic Virus",
+            "10": "Tomato Yellow Leaf Curl Virus"
+        },
+        "ml_task": "image_classification",
+        "ag_task": "disease_classification",
+        "location": {
+            "continent": "worldwide",
+            "country": "worldwide"
+        },
+        "sensor_modality": "rgb",
+        "real_synthetic": "real",
+        "platform": "handheld",
+        "input_data_format": [
+            "jpeg"
+        ],
+        "annotation_format": "directory_names",
+        "n_images": "11000",
+        "docs_url": "https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf?resource=download"
+    },
     "vine_virus_photo_dataset": {
         "classes": {
             "1": "Leafroll 3",

agml/_assets/source_citations.json

@@ -151,6 +151,10 @@
         "license": "",
         "citation": "Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N. PlantDoc: a dataset for visual plant disease detection. InProceedings of the 7th ACM IKDD CoDS and 25th COMAD 2020 Jan 5 (pp. 249-253)."
     },
+    "tomato_leaf_disease": {
+        "license": "CC0: Public Domain",
+        "citation": ""
+    },
     "vine_virus_photo_dataset": {
         "license": "Apache 2.0",
         "citation": ""

agml/_internal/preprocess.py

@@ -140,6 +140,51 @@ def corn_maize_leaf_disease(self, dataset_name):
                         processed_image.save(os.path.join(output_path, cls, img))
 
         print(f"Dataset {dataset_name} has been preprocessed and saved to {output_path}")
+
+    def tomato_leaf_disease(self, dataset_name):
+        """Preprocesses the Tomato Leaf Disease Dataset."""
+        # Get the dataset directory directly (no need for 'original' directory)
+        base_path = self.data_dir
+        classes = sorted([d for d in os.listdir(base_path) if os.path.isdir(os.path.join(base_path, d))])
+
+        # Create output directory
+        output_path = os.path.join(self.data_processed_dir, dataset_name)
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+
+        # Create subdirectories for each class inside the output directory
+        for cls in classes:
+            class_output_path = os.path.join(output_path, cls)
+            if not os.path.exists(class_output_path):
+                os.makedirs(class_output_path)
+
+        # Process and copy the dataset images to the processed directory
+        for cls in classes:
+            class_path = os.path.join(base_path, cls)
+            for img in os.listdir(class_path):
+                if img.endswith(('jpg', 'png', 'jpeg', 'JPG')):
+                    img_path = os.path.join(class_path, img)
+
+                    # Open the image using Pillow
+                    with Image.open(img_path) as image:
+                        # Convert image to a NumPy array
+                        img_array = np.array(image)
+
+                        # Ensure the image is in range [0, 255] and dtype is uint8
+                        if img_array.dtype != np.uint8:
+                            # If the image is in float [0-1], scale it to [0-255]
+                            img_array = (img_array * 255).astype(np.uint8)
+
+                        # Convert back to PIL Image to save
+                        processed_image = Image.fromarray(img_array)
+
+                        if processed_image.mode == 'RGBA':
+                            processed_image = processed_image.convert('RGB')
+
+                        # Save the processed image to the output directory
+                        processed_image.save(os.path.join(output_path, cls, img))
+
+        print(f"Dataset {dataset_name} has been preprocessed and saved to {output_path}")
 
     def bean_disease_uganda(self, dataset_name):
         # Get the dataset classes and paths