A Systematic Study on Intelligent Mango Harvesting Schemes

Mango harvesting remains a labor-intensive and inefficient task, especially for large-scale orchards. This study investigates three intelligent harvesting schemes for the Jinhuang mango: twisting with basket collection, cutting with basket collection, and cutting with slideway collection. SolidWorks and ADAMS simulations are used to evaluate their performance in terms of gripping damage force, post-harvest collision force, and harvesting time. Results show that while the cutting–basket scheme minimizes gripping damage, the cutting–slideway scheme offers the best overall balance between fruit protection and efficiency. This study provides a preliminary framework for evaluating robotic mango harvesting schemes and highlights the need for further optimization and field validation.