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Accelerating image-based plant phenotyping and pattern recognition: deep learning or few-shot learning?

  1. The application of autopilot technology is conductive to achieving path planning navigation and liberating labor productivity. In addition, the self-driving vehicles can drive according to the growth state of ...

    Authors: Xihuizi Liang, Bingqi Chen, Chaojie Wei and Xiongchu Zhang
    Citation: Plant Methods 2022 18:90
  2. Methods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over tim...

    Authors: Kiona Elliott, Jeffrey C. Berry, Hobin Kim and Rebecca S. Bart
    Citation: Plant Methods 2022 18:86
  3. The superposition of COVID-19 and climate change has brought great challenges to global food security. As a major economic crop in the world, studying its phenotype to cultivate high-quality wheat varieties is...

    Authors: Anhua Ren, Dong Jiang, Min Kang, Jie Wu, Fangcheng Xiao, Pei Hou and Xiuqing Fu
    Citation: Plant Methods 2022 18:77
  4. The number of banana plants is closely related to banana yield. The diameter and height of the pseudo-stem are important morphological parameters of banana plants, which can reflect the growth status and vital...

    Authors: Yanlong Miao, Liuyang Wang, Cheng Peng, Han Li, Xiuhua Li and Man Zhang
    Citation: Plant Methods 2022 18:66
  5. The accurate estimation of leaf hydraulic conductance (Kleaf) is important for revealing leaf physiological characteristics and function. However, the Kleaf values are largely incomparable in previous studies for...

    Authors: Xiaoxiao Wang, Jinfang Zhao, Jianliang Huang, Shaobing Peng and Dongliang Xiong
    Citation: Plant Methods 2022 18:63
  6. From an economic perspective, cotton is one of the most important crops in the world. The fertility of male reproductive organs is a key determinant of cotton yield. Anther dehiscence or indehiscence directly ...

    Authors: Zhihao Tan, Jiawei Shi, Rongjie Lv, Qingyuan Li, Jing Yang, Yizan Ma, Yanlong Li, Yuanlong Wu, Rui Zhang, Huanhuan Ma, Yawei Li, Li Zhu, Longfu Zhu, Xianlong Zhang, Jie Kong, Wanneng Yang…
    Citation: Plant Methods 2022 18:53
  7. Anthracnose of Camellia oleifera is a very destructive disease that commonly occurs in the Camellia oleifera industry, which severely restricts the development of the Camellia oleifera industry. In the early stag...

    Authors: Li Bin, Wang Qiu, Zhan Chao-hui, Han Zhao-yang, Yin Hai, Liao Jun and Liu Yan-de
    Citation: Plant Methods 2022 18:52
  8. With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massiv...

    Authors: Jiachen Yang, Xiaolan Guo, Yang Li, Francesco Marinello, Sezai Ercisli and Zhuo Zhang
    Citation: Plant Methods 2022 18:28
  9. In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally ...

    Authors: Gabriella E. C. Gall, Talmo D. Pereira, Alex Jordan and Yasmine Meroz
    Citation: Plant Methods 2022 18:21
  10. Plant plasma membrane transporters play essential roles during the translocation of vectorized agrochemicals. Therefore, transporters associated with phloem loading of vectorized agrochemicals have drawn incre...

    Authors: Yongxin Xiao, Jinying Zhang, Yiting Li, Tom Hsiang, Xingping Zhang, Yongxing Zhu, Xiaoying Du, Junliang Yin and Junkai Li
    Citation: Plant Methods 2022 18:11
  11. Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton...

    Authors: Vivien Rolland, Moshiur R. Farazi, Warren C. Conaty, Deon Cameron, Shiming Liu, Lars Petersson and Warwick N. Stiller
    Citation: Plant Methods 2022 18:8
  12. Due to the high cost of data collection for magnetization detection of media, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical ...

    Authors: Jing Nie, Nianyi Wang, Jingbin Li, Kang Wang and Hongkun Wang
    Citation: Plant Methods 2021 17:119
  13. Realizing imaging detection of water and nitrogen content in different regions of plant leaves in-site and real-time can provide an efficient new technology for determining crop drought resistance and nutrient...

    Authors: Ning Zhang, Peng-cheng Li, Hubin Liu, Tian-cheng Huang, Han Liu, Yu Kong, Zhi-cheng Dong, Yu-hui Yuan, Long-lian Zhao and Jun-hui Li
    Citation: Plant Methods 2021 17:117

Guest Editors:
Yang Li, Shihezi University, Xinjiang, China 
Jiachen Yang, Tianjin University, Tianjin, China
Francesco Marinello, University of Padova, Padova, Italy

In recent years, deep learning methods have played a great role in the plant sciences and achieved a series of remarkable achievements in many fields, such as yield prediction and estimation, crop pest identification, plant disease detection, physiological trait indication, seedling development monitoring, plant irrigation strategy, cultivar recognition, leaf counting, etc. However, the applications based on typical deep learning rely heavily on big-scale datasets requiring substantial manual annotation of training data, which is a serious shortcoming. After all, large-scale real-world agricultural datasets are time-consuming and expensive to collect and label by experts for every potential application. In order to alleviate this problem, few-shot learning is emerging, also called learning from few data. Few-shot learning is a new branch of deep learning, which aims to develop an intelligent model with good generalization from only few data, towards the combination of machine intelligence with flexibility and extensibility. Both deep learning and few-shot learning are technological explorations in the field of plant sciences that have the potential to greatly accelerate their applications. 

This page was last updated on March 2022.