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Related Concept Videos

Reproductive Cloning01:27

Reproductive Cloning

Reproductive cloning is the process of producing a genetically identical copy—a clone—of an entire organism. While clones can be produced by splitting an early embryo—similar to what happens naturally with identical twins—cloning of adult animals is usually done by a process called somatic cell nuclear transfer (SCNT).
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During fertilization, an egg and sperm cell fuse to create a new diploid structure. In humans, the process occurs once the egg has been released from the ovary, and travels into the fallopian tubes. The process requires several key steps: 1) sperm present in the genital tract must locate the egg; 2) once there, sperm need to release enzymes to help them burrow through the protective zona pellucida of the egg; and 3) the membranes of a single sperm cell and egg must fuse, with the sperm...
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Plant Breeding and Biotechnology

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Reproductive Cloning

Reproductive cloning is the process of producing a genetically identical copy—a clone—of an entire organism. While clones can be produced by splitting an early embryo—similar to what happens naturally with identical twins—cloning of adult animals is usually done by a process called somatic cell nuclear transfer (SCNT).
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Updated: Jun 28, 2026

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Trinocular Vision-Driven Robotic Fertilization: Enhanced YOLOv8n for Precision Mulberry Growth Synchronization.

Ma Ming1, Osama Elsherbiny1, Jianmin Gao1

  • 1School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.

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|May 14, 2025
PubMed
Summary

This study introduces an AI-powered foliar fertilizer system for mulberry cultivation. The intelligent system uses deep learning to precisely target and fertilize slow-growing branches, promoting synchronized seedling development.

Keywords:
deep learningfoliar fertilizermulberry cuttingsparallel roboticstrinocular visionupgraded YOLOv8n

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Deep Learning

Background:

  • Delayed root system development in mulberry trees is a significant challenge in aerosol cultivation.
  • Asynchronous growth of branches and buds contributes to this issue, hindering overall seedling development.

Purpose of the Study:

  • To develop an intelligent foliar fertilizer spraying system for promoting synchronous growth in mulberry seedlings.
  • To enhance the precision and efficiency of fertilization for lagging branches using deep learning and robotics.

Main Methods:

  • A parallel robotic arm spraying device integrated with trinocular vision for image acquisition.
  • An enhanced YOLOv8n deep learning model incorporating Asymptotic Feature Pyramid Network (AFPN), MSBlock, dynamic ATSS, and Focal_XIoU loss.
  • An artificial neural network for robotic arm coordinate calculation.

Main Results:

  • The enhanced YOLOv8n model achieved an average precision of 94.48%, a 6.05% improvement over the original model.
  • The robotic arm coordinate prediction error was maintained at less than or equal to 1.3%.
  • The system demonstrated effective precise location and directional fertilization of mulberry branches.

Conclusions:

  • The intelligent foliar fertilizer spraying system significantly promotes synchronous development of mulberry seedlings.
  • Deep learning-based computer vision and robotics offer a viable solution for precision agriculture challenges in plant cultivation.