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Dear Colleagues,

Robotics has changed many industries globally. These technologies will bring a huge productivity revolution to traditional industries with relatively low productivity, including the primary industry, agriculture. With the rapid development of artificial intelligence, robotics, intelligent sensing, flexible control, and other technologies, a growing number of researchers believe that intelligent robotics are expected to greatly liberate agricultural productivity. However, to achieve this goal, we still need to solve many technical challenges, including how to make the robot work in a variety of real environments, how to realize autonomous path planning, and how to improve the sensing ability of the robot.

The topics of interest within the scope of this focused section include but are not limited to:

Advanced machines or robotics for intelligent agriculture;
Soft-grasping/soft-robotics manipulators;
Fruit/vegetable detection and localization for automatic harvesting robots;
Crop yield estimation;
High-accuracy robot vision navigation technology.

Dr. Lixue Zhu

Dr. Chao Chen

Dr. Tang Yunchao

Dr. Liang Gong

Keywords

robotics
intelligent agriculture
autonomous path planning
crop yield estimation

Published Papers (2 papers)

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Research

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Open AccessArticle

A Fast and Accurate Obstacle Segmentation Network for Guava-Harvesting Robot via Exploiting Multi-Level Features

and

Sustainability 2022, 14(19), 12899; https://doi.org/10.3390/su141912899 - 10 Oct 2022

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Abstract

Guava fruit is readily concealed by branches, making it difficult for picking robots to rapidly grip. For the robots to plan collision-free paths, it is crucial to segment branches and fruits. This study investigates a fast and accurate obstacle segmentation network for guava-harvesting robots. At first, to extract feature maps of different levels quickly, Mobilenetv2 is used as a backbone. Afterwards, a feature enhancement module is proposed to fuse multi-level features and recalibrate their channels. On the basis of this, a decoder module is developed, which strengthens the connection between each position in the feature maps using a self-attention network, and outputs a dense segmentation map. Experimental results show that in terms of the mean intersection over union, mean pixel accuracy, and frequency weighted intersection over union, the developed network is 1.83%, 1.60% and 0.43% higher than Mobilenetv2-deeplabv3+, and 3.77%, 2.43% and 1.70% higher than Mobilenetv2-PSPnet; our network achieved an inference speed of 45 frames per second and 35.7 billion floating-point operations per second. To sum up, this network can realize fast and accurate semantic segmentation of obstacles, and provide strong technical and theoretical support for picking robots to avoid obstacles. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Its Application for Intelligent Agriculture)

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Other

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Open AccessTechnical Note

Fruit Phantoms for Robotic Harvesting Trials—Mango Example

Rafael Goulart

Dennis Jarvis

and

Kerry B. Walsh

Sustainability 2023, 15(3), 1789; https://doi.org/10.3390/su15031789 - 17 Jan 2023

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Abstract

Experimental trials on the performance of end-effectors for the automated harvest of soft fruit are constrained by seasonal limitations on fruit availability and fruit perishability, necessitating the use of different sets of fruit across time. Consequently, the use of fruit and stalk phantoms, rather than real fruit, is an attractive proposition. In this paper, a process for the cost-effective production of stable fruit phantoms using silicone (polydimethylsiloxane, PDMS) and starch was presented. A preliminary consideration was also presented for the creation of a phantom fruit stalk, involving a wooden dowel or a magnetic latching. Mango fruit phantoms were benchmarked to mango fruit in terms of density, firmness, brittleness, etc. Full article

(This article belongs to the Special Issue Recent Advances in Robotics and Its Application for Intelligent Agriculture)

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