Apple harvesting robot under information technology: A review

Jia, Weikuan; Yan, Zhang; Lian, Jian; Zheng, Yuanjie; Zhao, Debin; Li, Chengjiang

doi:10.1177/1729881420925310

Cited by 105 publications

(43 citation statements)

References 61 publications

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“…In terms of ground-level harvesting, the SW 6010 is the first autonomous robot which is available that can collect strawberries. The other system is called soft harvesting, where delicate suction cups are used to collect the fruits such as pineapples, apples [2], [3]. These robotic arms can also be combined hand to hand to provide better accuracy while gathering.…”

Section: Research Methods 21 Literature Reviewmentioning

confidence: 99%

Agricultural harvesting using integrated robot system

Raja¹,

B²,

Nagaraj³

et al. 2022

IJEECS

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In today's competitive world, robot designs are developed to simplify and improve quality wherever necessary. The rise in technology and modernization has led people from the unskilled sector to shift to the skilled sector. The agricultural sector's solution for harvesting fruits and vegetables is manual labor and a few other agro bots that are expensive and have various limitations when it comes to harvesting. Although robots present may achieve harvesting, the affordability of such designs may not be possible by small and medium-scale producers. The integrated robot system is designed to solve this problem, and when compared with the existing manual methods, this seems to be the most cost-effective, efficient, and viable solution. The robot uses deep learning for image detection, and the object is acquired using robotic manipulators. The robot uses a Cartesian and articulated configuration to perform the picking action. In the end, the robot is operated where carrots and cantaloupes were harvested. The data of the harvested crops are used to arrive at the conclusion of the robot's accuracy.

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Section: Research Methods 21 Literature Reviewmentioning

confidence: 99%

Agricultural harvesting using integrated robot system

Raja¹,

B²,

Nagaraj³

et al. 2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Birrell and colleagues used visual technology to locate, classify and harvest iceberg lettuce in a complex environment [98]. Visual control technology provides important technical support to the fruit and vegetable harvesting robots, and the research on picking robots in recognition, positioning, all-weather operation mode and intelligent computing indicates the possibility of robot application driven by information technology [99]. An unmanned farm relies on the application of these robots, but autonomous operating platforms are currently less efficient than mobile operating platforms with human participation.…”

Section: Agricultural Robots: Replace Farmersmentioning

confidence: 99%

From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production

Wang

et al. 2021

Agriculture

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Agriculture is the most important industry for human survival and solving the hunger problem worldwide. With the growth of the global population, the demand for food is increasing, which needs more agriculture labor. However, the number of people willing to engage in agricultural work is decreasing, causing a severe shortage of agricultural labor. Therefore, it is necessary to study the mode of agricultural production without labor force participation. With the rapid development of the Internet of Things, Big Data, artificial intelligence, robotics and fifth-generation (5G) communication technology, robots can replace humans in agricultural operations, thus enabling the establishment of unmanned farms in the near future. In this review, we have defined unmanned farms, introduced the framework of unmanned farms, analyzed the current state of the technology and how these technologies can be used in unmanned farms, and finally discuss all the technical challenges. We believe that this review will provide guidance for the development of unmanned farms and provide ideas for further investigation of these farms.

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“…The picking of tomatoes is time-consuming and labor-intensive work. The automatic harvesting robots have become a research hotspot, the first step of automatic picking is fruit detection and localization on plants using computer vision [ 8 , 9 , 10 ]. Despite extensive research, fruit detection systems are still a weak link, which limits the commercial application of harvesting robots [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Detection and Segmentation of Mature Green Tomatoes Based on Mask R-CNN with Automatic Image Acquisition Approach

Zhao

Liu

et al. 2021

Sensors

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Since the mature green tomatoes have color similar to branches and leaves, some are shaded by branches and leaves, and overlapped by other tomatoes, the accurate detection and location of these tomatoes is rather difficult. This paper proposes to use the Mask R-CNN algorithm for the detection and segmentation of mature green tomatoes. A mobile robot is designed to collect images round-the-clock and with different conditions in the whole greenhouse, thus, to make sure the captured dataset are not only objects with the interest of users. After the training process, RestNet50-FPN is selected as the backbone network. Then, the feature map is trained through the region proposal network to generate the region of interest (ROI), and the ROIAlign bilinear interpolation is used to calculate the target region, such that the corresponding region in the feature map is pooled to a fixed size based on the position coordinates of the preselection box. Finally, the detection and segmentation of mature green tomatoes is realized by the parallel actions of ROI target categories, bounding box regression and mask. When the Intersection over Union is equal to 0.5, the performance of the trained model is the best. The experimental results show that the F1-Score of bounding box and mask region all achieve 92.0%. The image acquisition processes are fully unobservable, without any user preselection, which are a highly heterogenic mix, the selected Mask R-CNN algorithm could also accurately detect mature green tomatoes. The performance of this proposed model in a real greenhouse harvesting environment is also evaluated, thus facilitating the direct application in a tomato harvesting robot.

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Apple harvesting robot under information technology: A review

Cited by 105 publications

References 61 publications

Agricultural harvesting using integrated robot system

Agricultural harvesting using integrated robot system

From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production

Detection and Segmentation of Mature Green Tomatoes Based on Mask R-CNN with Automatic Image Acquisition Approach

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