Next-Generation Harvester Technologies: Synergizing Smart Grading and Biomechanical Damage Control in Mechanized...

Area of Science:

• Agricultural Engineering
• Robotics
• Computer Vision

Background:

• Industrial tomato harvesting faces challenges with mechanical injuries and foreign material contamination from electro-mechanical combine harvesters.
• Current smart grading systems struggle with the complexities of open-field conditions and operational vibrations.

Purpose of the Study:

• To review recent advancements in harvester-mounted smart grading systems for industrial tomato harvesting.
• To explore the integration of edge computing and soft robotics for damage reduction and improved sorting.

Main Methods:

• Analysis of heterogeneous edge-computing frameworks (FPGAs, embedded GPUs) for real-time processing under vibration.
• Synthesis of multi-scale finite element simulations to understand crop microstructural failure modes.
• Investigation of multi-modal sensor fusion with Convolutional Neural Networks (CNNs) for non-destructive internal property evaluation.

Main Results:

• Edge-computing architectures show potential in mitigating processing delays caused by operational vibrations.
• Soft robotic effectors regulating applied forces can approach a 'damage-free' handling threshold.
• CNNs coupled with sensor fusion demonstrate promise for internal property evaluation despite environmental constraints.

Conclusions:

• Transitioning to advanced edge-computing and soft robotics is crucial for next-generation harvesting machinery.
• Synergy between agronomic practices and machine engineering is vital for developing resilient and gentle sorting systems.
• Further validation of CNN-based evaluation across diverse field datasets is needed.