A Review of Orchard Canopy Perception Technologies for Variable-Rate Spraying

Area of Science:

• Agricultural Engineering
• Precision Agriculture
• Robotics and Sensing

Background:

• Variable-rate spraying (VRS) technology offers enhanced pesticide efficiency and environmental sustainability in orchards.
• Orchard canopy structure significantly impacts spraying effectiveness, necessitating dynamic characterization.
• Advancements in precision agriculture drive the need for sophisticated canopy perception and modeling.

Purpose of the Study:

• To systematically review recent progress in dynamic perception and modeling of orchard canopies for VRS applications.
• To analyze the integration of canopy sensing technologies into VRS systems for optimized spray delivery.
• To identify challenges and future research directions in dynamic canopy modeling for precision agriculture.

Main Methods:

• Review of key sensing technologies: LiDAR, Vision Sensors, multispectral/hyperspectral sensors.
• Analysis of point cloud processing techniques for canopy data interpretation.
• Discussion of static, quasi-dynamic, and fully dynamic canopy modeling frameworks.

Main Results:

• Integration of sensing technologies enables improved spray path planning and nozzle control in VRS.
• Dynamic canopy models are crucial for precise droplet transport regulation.
• Challenges include balancing real-time performance, seasonal adaptability, and modeling accuracy.

Conclusions:

• Multimodal perception and hybrid modeling approaches (physics-based and data-driven) are key future directions.
• Intelligent control strategies are essential for optimizing VRS in dynamic orchard environments.
• Further research is needed to enhance real-time performance and seasonal adaptability of canopy models.