Sensing Modeling and Robots for Plants

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 2201

Special Issue Editor

College of Engineering, China Agricultural University, Beijing 100083, China
Interests: smart urban agriculture; artificial intelligence; agricultural robotics; automated control; unmanned aerial vehicle; plant phenotyping; computer vision; crop plant signaling; machine (deep) learning; food processing and safety; fluorescence imaging; hyper/multispectral imaging; Vis/NIR/MIR imaging spectroscopy
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Special Issue Information

Dear Colleagues,

Climate change poses a great threat to sustainable plant-based food production worldwide, but the rapid growth of human demand for foods requires that plant yields have to continue to increase each year. This requires making full use of existing land resources to ensure sustainable food production. As the global population continues to increase, fewer people are willing to work in agriculture. It is imperative to develop intelligent agricultural systems and robots, which can effectively alleviate the shortage of rural labor and improve agricultural production efficiency. The goal of smart agriculture is to achieve high-precision, high-efficiency, and green development of agricultural production, which requires smart agricultural equipment to be efficient, energy-saving, and environmentally friendly, and to improve the automation and intelligence level of agricultural operations. For future plant production, intelligent sensing and automatic control technology can be deeply integrated into agriculture, enabling automated perception of agricultural production information, quantitative decision-making, and intelligent control, thereby enabling precise input of agricultural inputs and providing customized services.

This Special Issue welcomes innovative research on intelligent sensing and automatic control technology for sustainable plant-based food production. We would like to invite experts and researchers in the field to contribute original and high-quality research articles and reviews to the journal (plants) peer-reviewed Special Issue: “Sensing Modeling and Robots for Plants”

Dr. Wen-Hao Su
Guest Editor

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Keywords

  • smart agriculture
  • artificial intelligence
  • intelligent machines
  • crop growth modeling
  • robot–plant interaction
  • high-throughput phenotyping
  • target detection
  • computer vision
  • remote sensing

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Published Papers (1 paper)

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Research

22 pages, 4243 KiB  
Article
High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision
by Qi-Yuan Zhang, Ke-Jun Fan, Zhixi Tian, Kai Guo and Wen-Hao Su
Plants 2024, 13(18), 2613; https://doi.org/10.3390/plants13182613 - 19 Sep 2024
Cited by 2 | Viewed by 1605
Abstract
The automated collection of plant phenotypic information has become a trend in breeding and smart agriculture. Four YOLOv8-based models were used to segment mature soybean plants placed in a simple background in a laboratory environment, identify pods, distinguish the number of soybeans in [...] Read more.
The automated collection of plant phenotypic information has become a trend in breeding and smart agriculture. Four YOLOv8-based models were used to segment mature soybean plants placed in a simple background in a laboratory environment, identify pods, distinguish the number of soybeans in each pod, and obtain soybean phenotypes. The YOLOv8-Repvit model yielded the most optimal recognition results, with an R2 coefficient value of 0.96 for both pods and beans, and the RMSE values were 2.89 and 6.90, respectively. Moreover, a novel algorithm was devised to efficiently differentiate between the main stem and branches of soybean plants, called the midpoint coordinate algorithm (MCA). This was accomplished by linking the white pixels representing the stems in each column of the binary image to draw curves that represent the plant structure. The proposed method reduces computational time and spatial complexity in comparison to the A* algorithm, thereby providing an efficient and accurate approach for measuring the phenotypic characteristics of soybean plants. This research lays a technical foundation for obtaining the phenotypic data of densely overlapped and partitioned mature soybean plants under field conditions at harvest. Full article
(This article belongs to the Special Issue Sensing Modeling and Robots for Plants)
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