---
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.