Decide SOW/Goal 23-M-09_GrapeCD

[robbiebbaker]

Grape cluster detection is a computer vision and image processing task that involves identifying and locating grape clusters within vineyard images or videos. This technology is particularly important for vineyards and farmers for several reasons:

1. Harvest Planning: Grape cluster detection helps vineyard owners and farmers plan their harvest more effectively. By knowing the location and quantity of grape clusters in advance, they can schedule the picking process efficiently, ensuring that grapes are harvested at the optimal time for quality and ripeness.
2. Yield Estimation: Accurate grape cluster detection allows for better yield estimation. Farmers can estimate the expected crop yield early in the growing season, helping with production planning, resource allocation, and market forecasting.
3. Quality Control: Identifying grape clusters enables farmers to monitor the health and quality of the grapes. Detection systems can identify clusters with signs of disease, pests, or other issues, allowing for targeted intervention and treatment.
4. Resource Optimization: Vineyards can optimize resource usage, such as labor and machinery, by directing them to the areas where grape clusters are most abundant. This reduces unnecessary manual labor and minimizes damage to the vines.
5. Grape Maturity Assessment: Grape cluster detection can be used to assess the maturity of the grapes. Different grape clusters may ripen at different rates, and this information can help determine when specific clusters should be harvested for the desired wine characteristics.
6. Precision Agriculture: Grape cluster detection is part of the broader field of precision agriculture. By using technology to precisely manage vineyard operations, farmers can reduce costs, increase productivity, and minimize environmental impacts.
7. Research and Development: In the research and development of new grape varieties or cultivation techniques, grape cluster detection can provide valuable data for analysis and experimentation.
8. Wine Quality: The quality of wine is closely tied to the quality of the grapes used in production. Grape cluster detection contributes to the production of high-quality grapes, which, in turn, leads to better-quality wines.
9. Sustainability: Efficient grape cluster detection can help minimize waste and optimize resource usage, contributing to sustainable farming practices and reduced environmental impact.

Grape cluster detection is typically achieved using computer vision techniques and machine learning algorithms that analyze images or videos captured in the vineyard. These systems can be automated and integrated into the overall vineyard management process, making them valuable tools for modern viticulture and wine production.

Requirements:

• Accuracy: accurately detect grape bunches, minimizing false positives and false negatives
• Real time processing: the model should be designed to process images or video frames in real-time or near real time to support timely decision making in vineyard operations
• Adaptable: The model should be adaptable to different vineyard environments and camera setups
• Integration: Ensure that the model can be integrated with different types of cameras, drones, or sensors commonly used in vineyards

Action Items:

• Research problem + current approaches
• Find existing datasets if possible
• See if problem can be simplified
• Discuss problem with project group
• Fill out problem solver document (clone from google drive)
• Discuss problem solver document with Robbie + your ideas