Strawberry Disease Detection Dataset

Content

The dataset consists of 2500 images in total with the corresponding segmentation annotation files for seven different types of strawberry diseases.

Applications of Strawberry Disease Detection Datasets

Strawberry disease detection datasets have numerous applications, including:

1. Precision Agriculture

Enabling the development of precision agriculture tools that can monitor crops in real-time, detect diseases early, and provide targeted treatment recommendations.

2. Research and Development

Facilitating research in plant pathology and helping develop new disease-resistant strawberry varieties through better understanding of disease dynamics.

3. Educational Tools

Serving as valuable resources for training students and professionals in plant pathology and agricultural sciences.

Challenges and Future Directions

Despite the advancements, several challenges remain in the field of strawberry disease detection:

1. Data Quality and Diversity

Ensuring the dataset is representative of real-world conditions, including different varieties of strawberries and diverse environmental conditions.

2. Integration with Other Technologies

Combining disease detection datasets with other technologies such as drones, IoT sensors, and weather data to create comprehensive crop management systems.

3. Continuous Improvement

Updating the datasets regularly with new images and annotations to keep up with emerging disease strains and changing agricultural practices.

Conclusion

The development and utilization of strawberry disease detection datasets are pivotal in advancing disease management and improving the sustainability of strawberry farming. By leveraging these datasets, we can build robust detection systems that aid farmers in protecting their crops, reducing losses, and ensuring a steady supply of high-quality strawberries.

Acknowledgements

This dataset is collected by the members of AI lab, Computer Science and Engineering department, JBNU. Permission is given to modify it as per your requirements and to use it as it is or to augment other datasets if needed.

Citation

Please cite the following paper in case you use it for your own research work.

Afzaal, U.; Bhattarai, B.; Pandeya, Y.R.; Lee, J. An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN. Sensors 2021, 21, 6565.