Development of a Machine Vision-Based Robotic Apple Crop Load Management System at Bud Stage

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Commodities

• Fruits: apples

Practices

• Crop Production: crop improvement and selection
• Education and Training: decision support system, extension

Apple crop load management is critical for balancing high yield and quality fruit production while ensuring sustainable tree health. Artificial Spur Extinction (ASE) is a new crop management technique that reduces the floral bud density of the tree canopy. This project aims to optimize the ASE process in apple orchards through advanced vision-based robotics, enhancing fruit quality and ensuring regular annual bearing. This technique employs automated, precise bud thinning based on the branch cross-sectional area (BCA) to achieve the desired fruit size. Our study follows three primary objectives: Firstly, developing a vision-based system for assessing bud density at the tree and branch levels enables accurate thinning decisions. Second, a decision support system for bud thinning will be developed based on the bud density and distributions at the branch and tree levels. Lastly, the robotic thinning system undergoes integration and evaluation through comprehensive field testing. Key to the success of this study is the engagement with the local agricultural community through a comprehensive outreach strategy. The technology will be demonstrated on field days targeted at local apple growers, and the findings and methodologies will be shared through extension talks and research publications. This approach facilitates the direct transfer of technology and encourages feedback for further refinement. The expected outcome is a proof of concept to showcase the feasibility and benefits of robotic bud thinning, potentially advancing apple orchard management and significantly boosting profitability through improved crop quality and yield.

This proposed research aims to develop a robotic bud thinning system for early crop load management in orchards, considering branches and tree levels for good-quality apples. The project will be structured around three key objectives:

Objective #1: Develop Deep Learning-Based Algorithms for Apple Bud Detection and Localization in Orchard Environments

Accurate detection and localization of apple buds within diverse orchard environments are foundational to precision agriculture practices, particularly in the context of bud thinning. This objective addresses a critical gap in current agricultural technology by developing sophisticated deep-learning algorithms that analyze visual data for bud density and specific locations. These algorithms are about identifying the buds and understanding the intricate dynamics of crop load at both branch and tree levels. The precision and effectiveness of bud thinning, directly influenced by the accuracy of these detections, can significantly affect the fruit quality and quantity, making this technology pivotal for sustainable and profitable orchard management.

Objective #2: Create a Decision Support System for Bud Thinning Based on the Bud Density and Distributions at the Branch and Tree Levels

Creating a decision support system (DSS) for bud thinning based on a comprehensive analysis of bud density and distribution represents a leap towards intelligent crop management. Such a system moves beyond the conventional one-size-fits-all approach to thinning, offering instead a nuanced, data-driven strategy that considers the unique characteristics of each tree and branch. This approach optimizes resource use and ensures that each action taken contributes to the overall health and productivity of the orchard. The DSS shifts from manual to data-driven decision-making, offering insights that boost thinning efficiency, cut labor costs, and enhance crop yield and quality.

Objective #3: Design and Test a Robotic End-Effector for Effective Bud Removal for Early Apple Crop Load Management

Designing and testing a robotic end-effector for efficient bud removal tackles one of the most labor-intensive and skill-dependent tasks in orchard management. Introducing such a device is not merely an advancement in mechanical design but a fundamental shift towards fully automated, precision agriculture. The effectiveness of bud thinning, crucial for controlling crop load and ensuring the development of high-quality fruit, depends significantly on the precision and gentleness of the removal process. A robotic end-effector that can quickly, accurately, and safely remove unwanted buds without damaging the tree opens the door to unprecedented efficiency and effectiveness in orchard management.