Improvements to and large‐scale evaluation of a robotic kiwifruit harvester

Williams, Henry; Ting, Canaan; Nejati, Mahla; Jones, Malcolm; Penhall, Nicky; Lim, JongYoon; Seabright, Matthew; Bell, Jamie; Ahn, Ho Seok; Scarfe, Alistair J.; Duke, Mike; MacDonald, Bruce A.

doi:10.1002/rob.21890

Cited by 77 publications

(35 citation statements)

References 38 publications

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“…A later improved robot [55] can detect 90% of the kiwi in the orchard, using Faster-Regions with Convolutional Neural Networks (RCCN). Specifically, 86.0% of kiwis can be picked by the improved device (55.8% of all kiwis).…”

Section: A Picking Robot With a Route Navigationmentioning

confidence: 99%

Review of smart robots for fruit and vegetable picking in agriculture

Yang

2022

International Journal of Agricultural and Biological Engineering

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The fruit and vegetable picking has posed a great challenge on the production and markets during the harvest season. Manual picking cannot fully meet the rapid requirements of each market, mainly due to the high labor-intensive and time-consuming tasks, even the aging and shortage of agricultural labor force in recent years. Alternatively, smart robotics can be an efficient solution to increase the planting areas for the markets in combination with changes in cultivation, preservation, and processing technology. However, some improvements still need to be performed on these picking robots. To document the progress in and current status of this field, this study performed a bibliometric analysis. This analysis evaluated the current performance characteristics of various fruit and vegetable picking robots for better prospects in the future. Five perspectives were proposed covering the robotic arms, end effectors, vision systems, picking environments, and picking performance for the large-scale mechanized production of fruits and vegetables in modern agriculture. The current problems of fruit and vegetable picking robots were summarized. Finally, the outlook of the fruit and vegetable picking robots prospected from four aspects: structured environment for fruit planting, the algorithm of recognition and positioning, picking efficiency, and cost-saving picking robots. This study comprehensively assesses the current research status, thus helping researchers steer their projects or locate potential collaborators.

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Section: A Picking Robot With a Route Navigationmentioning

confidence: 99%

Review of smart robots for fruit and vegetable picking in agriculture

Yang

2022

International Journal of Agricultural and Biological Engineering

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“…Reducing the cycle time for harvesting represents a vital role for the robot industry. The review showed a kiwi harvester could achieved the shortest cycle time of 1 s due to a low-cost and effective manipulator [52,53]. The damage caused during the grasping of the fruit is one of the main concerns in dealing with manipulators [54].…”

Section: Unmanned Ground Vehicles (Ugvs)mentioning

confidence: 99%

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

et al. 2021

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Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

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“…Williams, Zhang, SEPÚLVEDA, Mu, Chen, Li, and Liu et al [64][65][66][67][68][69][70][71][72][73][74][75][76] , have developed multi-arm robots for kiwifruit, tomato and grape harvesting and other high-density fruit respectively. Facing the new demand of the new urban production-leisure integrated industry, different humanoid dual-arm harvesting robots have been developed by Peng, and Chen et al [69,70] , which adopt parallel operation mode with the help of on-head RGB-D cameras and in-hand RGB-D cameras.…”

Section: Commercial Non-selective Agricultural Working Robotsmentioning

confidence: 99%

Development status and trend of agricultural robot technology

Jin¹,

Liu²,

Xu³

et al. 2021

International Journal of Agricultural and Biological Engineering

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In the face of the contradiction between the increasing demand for agricultural products and the sharp reduction of agricultural resources and labor force, agricultural robot technology is developing explosively on the basis of decades of technical and industrial exploration. In view of the complexity and particularity of the development of agricultural robot technology, it is of great value to summarize its development characteristics and make reasonable judgments on its development trend. In this paper, the type of agricultural robot systems was first discussed. From the classification of agricultural robot systems, the development of main types of monitoring robots, non-selective and selective working robots for crop farming, livestock and poultry farming and aquaculture were introduced in detail. Then the scientific research, core technology, and commercialization of different types of agricultural robots were summarized. It is believed that navigation in complex agricultural environments, damage-free robot-crop interaction, and agronomy-robot fusion have high scientific value and significance to promote the revolutionary advances in agricultural robot technology. The characteristics of inter-discipline between agricultural robot technology and new materials, artificial intelligence, bionics, agronomy are research focus. The fast damage-free operation, autonomous navigation for complex environments, target detection for complex backgrounds, and special design for agricultural robots are considered to be the key technology of agricultural robot development, and the development path is given. Finally, robot-crop interaction simulation, big data support, and artificial intelligence are regarded as paths to realize the breakthrough of key agricultural robot technologies. The summary and prospect of this paper are of positive significance to promote the overall development of agricultural robot technology.

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Improvements to and large‐scale evaluation of a robotic kiwifruit harvester

Cited by 77 publications

References 38 publications

Review of smart robots for fruit and vegetable picking in agriculture

Review of smart robots for fruit and vegetable picking in agriculture

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Development status and trend of agricultural robot technology

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