Development, integration, and field evaluation of an autonomous citrus‐harvesting robot

Abstract

Citrus harvesting is a labor-intensive and time-intensive task. As the global population continues to age, labor costs are increasing dramatically. Therefore, the citrus-harvesting robot has attracted considerable attention from the business and academic communities. However, robotic harvesting in unstructured and natural citrus orchards remains a challenge. This study aims to address some challenges faced in commercializing citrus-harvesting robots. We present a fully integrated, autonomous, and innovative solution for citrus-harvesting robots to overcome the harvesting difficulties derived from the natural growth characteristics of citrus. This solution uses a fused simultaneous localization and mapping algorithm based on multiple sensors to perform high-precision localization and navigation for the robot in the field orchard. Besides, a novel visual method for estimating fruit poses is proposed to cope with the randomization of citrus growth orientations. Further, a new end-effector is designed to improve the success and conformity rate of citrus stem cutting. Finally, a fully autonomous harvesting robot system has been developed and integrated. Field evaluations showed that the robot could harvest citrus continuously with an overall success rate of 87.2% and an average picking time of 10.9 s/fruit. These efforts provide a solid foundation for the future commercialization of citrus-harvesting robots.

In-Depth Evaluation of Automated Fruit Harvesting in Unstructured Environment for Improved Robot Design

“…Comparing the results with other recent field-tested fruit-harvesting robots, we find that Yin et al [23] tested a citrus-harvesting robot in the actual field with a success rate of 87.2% and a pick up time of 10.9 s. Furthermore, Zhang et al [47] harvested apples in the…”

“…Xiong et al [21] picked strawberries from a farm with a success rate up to 97.1% and a pick-up speed of 6.1 s. Similarly, Wang et al [22] tested apple harvesting in an orchard with a success rate of 70.77%. Moreover, Yin et al [23] harvested citrus with a successful picking rate of 87.2% and a cycle duration of 10.9 s. Williams et al [24] also harvested kiwi with a success rate of 86% and cycle duration of 2.78 s. Also, Lili et al [25] harvested tomatoes in a greenhouse with a detection rate of 99%, success rate of 86%, and cycle time of 15 s. Table 1 shows the recent fruit detection algorithms used in fruit-harvesting robots and their results. Convolutional neural networks (CNNs) are a type of deep learning architecture specifically designed for processing grid-like data for tasks like image classification.…”

“…P. Kurtser [43] sweet pepper RRT Suitable for real-time, non-optimal P. Kurtser [43] sweet pepper GA Optimal path, flexible, adaptive Y. Liu [23] tomato ACO Optimal path, robust H. Sarabu [44] apple RRT Efficient, suitable for real-time L. Wang [45] apple BSO Global optimization, robust Magalhães [46] grapes BiT RRT Efficient, complex implementation Convolutional neural networks (CNNs) are a type of deep learning architecture specifically designed for processing grid-like data for tasks like image classification. They utilize mathematical operations such as convolutions and pooling to extract hierarchical features from input images, followed by fully connected layers for classification or regression.…”

Leveraging I4.0 smart methodologies for developing solutions for harvesting produce

“…Simultaneous Localization and Mapping (SLAM) is the most applied algorithm, allowing the harvesting units to map out their environment and localize in real time. SLAM maps are constructed using point cloud data (Yin et al, 2023). Solutions for crops grown in unprotected environments were more likely to incorporate this element than those in protected environments.…”

“…A similar issue was noted by Brown and Sukkarieh (2021), where the oversized soft gripper design caused issues with plum picking and concerns with longevity resulting from damage of the silicon material. Difficulties in positioning were observed by Arad et al (2020) and Yin et al (2023) when obstacles and neighbouring fruit blocked the endeffector from reaching its intended picking position. Weight is also a crucial factor as it can significantly impact the cycle time and risk damaging the crop during harvest, which was highlighted in a solution for lettuce (Birrell et al, 2020).…”