Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+- object-detection
+- image-segmentation
+- image-classification
+language:
+- en
+tags:
+- agriculture
+- computer-vision
+- fruit-detection
+- instance-segmentation
+- precision-agriculture
+- ripeness-assessment
+- agricultural-robotics
+size_categories:
+- 1K<n<10K
+---
+
+# SmartHarvest: Multi-Species Fruit Ripeness Detection Dataset
+
+## Dataset Description
+
+**SmartHarvest** is a comprehensive multi-species fruit ripeness detection and segmentation dataset designed for precision agriculture applications. The dataset contains high-resolution images of fruits in natural garden environments with detailed polygon-based instance segmentation annotations and ripeness classifications.
+
+### Key Features
+- **8 fruit species**: Apple, cherry, cucumber, strawberry, tomato, plum, raspberry, pepper
+- **Multi-class ripeness**: Ripe, unripe, spoiled, plus obscured category
+- **Instance segmentation**: Polygon annotations with 3-126 vertices per instance
+- **Real-world conditions**: Natural lighting, occlusion, and clustering challenges
+- **Expert validation**: Agricultural specialist annotation review and quality control
+
+### Dataset Statistics
+- **Total images**: 486 high-resolution images
+- **Total annotations**: 6,984 individual fruit instances  
+- **Average annotations per image**: 14.4 instances
+- **Polygon complexity**: 14.1 ± 9.8 vertices per annotation
+- **Occlusion coverage**: 53.8% partially obscured instances
+- **Image resolution**: Resized and padded to 1200×1200 pixels
+
+## Supported Tasks
+
+### Primary Tasks
+- **Object Detection**: Fruit localization with species and ripeness classification
+- **Instance Segmentation**: Pixel-level fruit boundary delineation
+- **Multi-class Classification**: Combined species and ripeness state prediction
+
+### Agricultural Applications
+- **Robotic Harvesting**: Automated fruit picking with quality assessment
+- **Yield Prediction**: Crop monitoring and harvest optimization
+- **Quality Control**: Post-harvest sorting and grading
+- **Precision Agriculture**: Species-specific crop management
+
+## Dataset Structure
+
+### Data Fields
+
+Each sample contains:
+
+```python
+{
+    'image': PIL.Image,                    # Original fruit image
+    'image_id': int,                       # Unique image identifier
+    'annotations': [
+        {
+            'id': int,                     # Unique annotation ID
+            'category_id': int,            # Species-ripeness category
+            'species': str,                # Fruit species name
+            'ripeness': str,               # Ripeness state
+            'bbox': [x, y, width, height], # Bounding box coordinates
+            'segmentation': [[x1,y1, ...]], # Polygon vertices
+            'area': float,                 # Annotation area in pixels²
+            'iscrowd': bool,               # Multiple objects flag
+            'visibility': str              # Occlusion status
+        }
+    ],
+    'metadata': {
+        'source': str,                     # Image source information
+        'capture_conditions': str,         # Lighting and environment
+        'quality_score': float            # Annotation quality metric
+    }
+}
+```
+
+### Category Mapping
+
+| Category ID | Species | Ripeness | Description |
+|-------------|---------|-----------|-------------|
+| 0 | background | - | Background class |
+| 1 | apple | unripe | Green/immature apples |
+| 2 | apple | ripe | Harvest-ready apples |
+| 3 | apple | spoiled | Overripe/damaged apples |
+| 4 | cherry | unripe | Immature cherries |
+| 5 | cherry | ripe | Harvest-ready cherries |
+| 6 | cherry | spoiled | Overripe cherries |
+| 7 | cucumber | unripe | Small/immature cucumbers |
+| 8 | cucumber | ripe | Harvest-ready cucumbers |
+| 9 | cucumber | spoiled | Overripe cucumbers |
+| 10 | strawberry | unripe | White/green strawberries |
+| 11 | strawberry | ripe | Red strawberries |
+| 12 | strawberry | spoiled | Overripe strawberries |
+| 13 | tomato | unripe | Green tomatoes |
+| 14 | tomato | ripe | Red tomatoes |
+| 15 | tomato | spoiled | Overripe tomatoes |
+
+*Additional species (plums, raspberries, peppers) in development*
+
+## Dataset Splits
+
+### Current Distribution
+- **Total**: 486 images with 6,984 annotations
+- **Apple subset**: 98 images, 2,582 annotations  
+- **Cherry subset**: 86 images, 969 annotations
+- **Tomato subset**: 94 images, 1,572 annotations
+- **Strawberry subset**: 111 images, 1,397 annotations
+- **Cucumber subset**: 97 images, 464 annotations
+
+### Recommended Splits
+For reproducible experiments, we recommend:
+- **Training**: 80% (389 images)
+- **Validation**: 20% (97 images)
+- **Stratification**: Balanced across species and ripeness states
+
+## Data Collection and Annotation
+
+### Collection Methodology
+- **Sources**: Natural garden environments, orchard partnerships
+- **Geographic coverage**: Multiple growing regions to reduce bias
+- **Temporal coverage**: Different seasons and growth stages  
+- **Lighting conditions**: Natural outdoor lighting with time-of-day variation
+- **Image quality**: High-resolution captures with professional equipment
+
+### Annotation Protocol
+- **Tool**: VGG Image Annotator (VIA) with custom configuration
+- **Annotators**: Trained computer vision researchers with agricultural consultation
+- **Quality control**: 25% overlap for inter-annotator agreement (κ > 0.85)
+- **Expert review**: 10% agricultural specialist validation
+- **Polygon precision**: Minimum 8 vertices, detailed boundary delineation
+
+### Species-Specific Criteria
+
+#### Color-Based Ripeness (Apples, Tomatoes, Cherries, Peppers)
+- **Ripe**: >75% characteristic color coverage
+- **Unripe**: <25% color development  
+- **Spoiled**: Brown/black discoloration, visible mold
+
+#### Size-Based Ripeness (Cucumbers, Pears)
+- **Ripe**: 80-100% of variety-specific size range
+- **Unripe**: <80% expected size
+- **Spoiled**: Yellowing, soft spots, wrinkled skin
+
+#### Texture-Based Ripeness (Strawberries, Raspberries)
+- **Ripe**: Uniform color, firm but yielding texture
+- **Unripe**: White/green areas, hard texture
+- **Spoiled**: Soft spots, mold, collapsed structure
+
+## Usage Examples
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load complete dataset
+dataset = load_dataset("TheCoffeeAddict/SmartHarvest")
+
+# Load specific split
+train_data = load_dataset("TheCoffeeAddict/SmartHarvest", split="train")
+
+# Access sample
+sample = dataset['train'][0]
+image = sample['image']
+annotations = sample['annotations']
+```
+
+### PyTorch Integration
+
+```python
+import torch
+from torch.utils.data import Dataset
+from torchvision import transforms
+from datasets import load_dataset
+
+class SmartHarvestDataset(Dataset):
+    def __init__(self, split="train", transform=None):
+        self.dataset = load_dataset("TheCoffeeAddict/SmartHarvest", split=split)
+        self.transform = transform
+        
+    def __len__(self):
+        return len(self.dataset)
+        
+    def __getitem__(self, idx):
+        sample = self.dataset[idx]
+        image = sample['image']
+        target = {
+            'boxes': torch.tensor(sample['bboxes']),
+            'labels': torch.tensor(sample['labels']),
+            'masks': torch.tensor(sample['masks'])
+        }
+        
+        if self.transform:
+            image = self.transform(image)
+            
+        return image, target
+
+# Usage
+transform = transforms.Compose([
+    transforms.Resize((800, 800)),
+    transforms.ToTensor(),
+])
+
+dataset = SmartHarvestDataset(split="train", transform=transform)
+```
+
+### Data Visualization
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+def visualize_sample(sample):
+    image = sample['image']
+    annotations = sample['annotations']
+    
+    fig, ax = plt.subplots(1, 1, figsize=(12, 8))
+    ax.imshow(image)
+    
+    for ann in annotations:
+        # Draw bounding box
+        x, y, w, h = ann['bbox']
+        rect = plt.Rectangle((x, y), w, h, fill=False, color='red', linewidth=2)
+        ax.add_patch(rect)
+        
+        # Add label
+        species = ann['species']
+        ripeness = ann['ripeness']
+        ax.text(x, y-5, f"{species}-{ripeness}", color='red', fontsize=10)
+    
+    ax.set_title("SmartHarvest Sample Annotation")
+    plt.show()
+
+# Visualize first sample
+sample = dataset['train'][0]
+visualize_sample(sample)
+```
+
+## Baseline Results
+
+### Model Performance (Apple-Cherry Subset)
+
+Trained Mask R-CNN with ResNet-50 backbone:
+
+| Metric | Value | Description |
+|--------|-------|-------------|
+| **AP@0.5** | **22.49%** | Average precision at IoU=0.5 |
+| **AP@0.75** | **7.98%** | Average precision at IoU=0.75 |
+| **COCO mAP** | **60.63%** | Mean AP across IoU 0.5-0.95 |
+
+### Per-Class Performance
+
+| Class | AP@0.5 | Notes |
+|-------|--------|--------|
+| Apple-Ripe | 10.45% | Challenging due to color variation |
+| Apple-Unripe | 25.00% | Better defined characteristics |
+| Apple-Spoiled | **32.60%** | Distinctive visual features |
+| Cherry-Ripe | 18.20% | Small size challenges |
+| Cherry-Unripe | 17.10% | Consistent with apple pattern |
+| Cherry-Spoiled | **31.56%** | Best performance per species |
+
+*Code available at: https://github.com/Maksim3l/SmartHarvest*
+
+## Considerations for Use
+
+### Strengths
+- **Real-world applicability**: Natural garden conditions with authentic challenges
+- **Multi-species coverage**: Broad agricultural applicability  
+- **Expert validation**: Agricultural specialist involvement in annotation
+- **Detailed annotations**: Polygon-level segmentation for precise localization
+- **Ripeness granularity**: Practical quality assessment categories
+
+### Limitations
+- **Geographic bias**: Limited to specific growing regions
+- **Seasonal bias**: Collection timing affects ripeness distribution
+- **Equipment bias**: Single camera system characteristics
+- **Scale limitations**: Limited images per species for production deployment
+- **Class imbalance**: Varying representation across ripeness states
+
+### Recommended Applications
+- **Research benchmarking**: Computer vision method evaluation
+- **Algorithm development**: Detection and segmentation model training
+- **Educational use**: Agricultural computer vision teaching
+- **Prototype development**: Proof-of-concept agricultural systems
+
+### Usage Considerations
+- **Data augmentation**: Recommended for training robustness
+- **Cross-validation**: Stratified splits to maintain species balance
+- **Evaluation metrics**: Use agricultural-relevant metrics beyond standard CV measures
+- **Deployment testing**: Validate on target agricultural environments
+
+## Ethical Considerations
+
+### Data Privacy
+- **Image sources**: Publicly available images or consent-obtained private collections
+- **Location privacy**: No GPS coordinates or specific farm identifiers included
+- **Farmer consent**: Proper permissions obtained for orchard data collection
+
+### Bias and Fairness
+- **Geographic diversity**: Active efforts to include multiple growing regions
+- **Seasonal representation**: Multiple collection periods to reduce temporal bias
+- **Equipment standardization**: Documentation of capture conditions for bias awareness
+
+### Environmental Impact
+- **Sustainable agriculture**: Supporting precision farming for reduced resource use
+- **Technology access**: Open-source approach for global accessibility
+- **Local adaptation**: Encouragement of regional dataset development
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@inproceedings{loknar2025comprehensive,
+    title={Comprehensive Multi-Species Fruit Ripeness Dataset Construction: From Eight-Species Collection to Focused Apple-Cherry Detection},
+    author={Loknar, Maksim and Mlakar, Uroš},
+    booktitle={Student Computing Research Symposium},
+    year={2025},
+    organization={University of Maribor},
+    url={https://huggingface.co/datasets/TheCoffeeAddict/SmartHarvest}
+}
+```
+
+## Dataset Card Contact
+
+**Authors**: Maksim Loknar, Uroš Mlakar  
+**Institution**: Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia  
+**Email**: maksim.loknar@student.um.si, uros.mlakar@um.si  
+**Project Page**: https://github.com/Maksim3l/SmartHarvest
+
+For questions about dataset usage, additional species requests, or collaboration opportunities, please open an issue in the GitHub repository or contact the authors directly.