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

In this topical collection (18 articles)

1. Research

   Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM

   Jing Nie, Nianyi Wang, Jingbin Li, Kang Wang, Hongkun Wang Article:119

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2. Research

   Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter

   Ning Zhang, Peng-cheng Li, Hubin Liu, Tian-cheng Huang, Han Liu, Yu Kong… Article:117

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3. Research

   Few-shot cotton leaf spots disease classification based on metric learning

   Xihuizi Liang Article:114

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4. Research

   Experimental study on creep properties prediction of reed bales based on SVR and MLP

   Jixia Li, Lixin Zhang, Guangdi Huang, Huan Wang, Youzhong Jiang Article:112

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5. Research

   Inter-row navigation line detection for cotton with broken rows

   Xihuizi Liang, Bingqi Chen, Chaojie Wei, Xiongchu Zhang Article:90

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6. Methodology

   A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity

   Kiona Elliott, Jeffrey C. Berry, Hobin Kim, Rebecca S. Bart Article:86

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7. Research

   Depth image conversion model based on CycleGAN for growing tomato truss identification

   Dae-Hyun Jung, Cheoul Young Kim, Taek Sung Lee, Soo Hyun Park Article:83

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8. Research

   Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat

   Anhua Ren, Dong Jiang, Min Kang, Jie Wu, Fangcheng Xiao, Pei Hou… Article:77

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9. Research

   Semi-supervised few-shot learning approach for plant diseases recognition

   Yang Li, Xuewei Chao Article:68

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10. Research

    Banana plant counting and morphological parameters measurement based on terrestrial laser scanning

    Yanlong Miao, Liuyang Wang, Cheng Peng, Han Li, Xiuhua Li, Man Zhang Article:66

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11. Research

    Evaporative flux method of leaf hydraulic conductance estimation: sources of uncertainty and reporting format recommendation

    Xiaoxiao Wang, Jinfang Zhao, Jianliang Huang, Shaobing Peng… Article:63

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12. Research

    Yield estimation of high-density cotton fields using low-altitude UAV imaging and deep learning

    Fei Li, Jingya Bai, Mengyun Zhang, Ruoyu Zhang Article:55

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13. Research

    Fast anther dehiscence status recognition system established by deep learning to screen heat tolerant cotton

    Zhihao Tan, Jiawei Shi, Rongjie Lv, Qingyuan Li, Jing Yang, Yizan Ma… Article:53

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14. Research

    Research on anthracnose grade of Camellia oleifera based on the combined LIBS and THz technology

    Li Bin, Wang Qiu, Zhan Chao-hui, Han Zhao-yang, Yin Hai, Liao Jun… Article:52

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15. Review

    A survey of few-shot learning in smart agriculture: developments, applications, and challenges

    Jiachen Yang, Xiaolan Guo, Yang Li, Francesco Marinello, Sezai Ercisli… Article:28

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16. Research

    Fast estimation of plant growth dynamics using deep neural networks

    Gabriella E. C. Gall, Talmo D. Pereira, Alex Jordan, Yasmine Meroz Article:21

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17. Methodology

    An efficient overexpression method for studying genes in Ricinus that transport vectorized agrochemicals

    Yongxin Xiao, Jinying Zhang, Yiting Li, Tom Hsiang, Xingping Zhang… Article:11

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18. Methodology

    HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance

    Vivien Rolland, Moshiur R. Farazi, Warren C. Conaty, Deon Cameron… Article:8

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