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Early Identification and Localization Algorithm for Weak Seedlings Based on Phenotype Detection and Machine Learning

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  • Shengyong Xu

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
    Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China
    Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China)

  • Yi Zhang

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
    Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China
    Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China)

  • Wanjing Dong

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
    Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China
    Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China)

  • Zhilong Bie

    (College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China)

  • Chengli Peng

    (Electronic Information School, Wuhan University, Wuhan 430072, China)

  • Yuan Huang

    (College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China)

Abstract

It is important to propose the correct decision for culling and replenishing seedlings in factory seedling nurseries to improve the quality of seedlings and save resources. To solve the problems of inefficiency and subjectivity of the existing traditional manual culling and replenishment of seeds, this paper proposes an automatic method to discriminate the early growth condition of seedlings. Taking watermelon plug seedlings as an example, Azure Kinect was used to collect data of its top view three times a day, at 9:00, 14:00, and 19:00. The data were collected from the time of germination to the time of main leaf growth, and the seedlings were manually determined to be strong or weak on the last day of collection. Pre-processing, image segmentation, and point cloud processing methods were performed on the collected data to obtain the plant height and leaf area of each seedling. The plant height and leaf area on the sixth day were predicted using an LSTM recurrent neural network for the first three days. The R squared for plant height and leaf area prediction were 0.932 and 0.901, respectively. The dichotomous classification of normal and abnormal seedlings was performed using six machine learning classification methods, such as random forest, SVM, and XGBoost, for day six data. The experimental results proved that random forest had the highest classification accuracy of 84%. Finally, the appropriate culling and replenishment decisions are given based on the classification results. This method can provide some technical support and a theoretical basis for factory seedling nurseries and transplanting robots.

Suggested Citation

  • Shengyong Xu & Yi Zhang & Wanjing Dong & Zhilong Bie & Chengli Peng & Yuan Huang, 2023. "Early Identification and Localization Algorithm for Weak Seedlings Based on Phenotype Detection and Machine Learning," Agriculture, MDPI, vol. 13(1), pages 1-19, January.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:1:p:212-:d:1036063
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    References listed on IDEAS

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    1. Hongru Wang & Jun He & Noshaba Aziz & Yue Wang, 2022. "Spatial Distribution and Driving Forces of the Vegetable Industry in China," Land, MDPI, vol. 11(7), pages 1-18, June.
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    Cited by:

    1. Peisen Yuan & Shuning Xu & Zhaoyu Zhai & Huanliang Xu, 2023. "Phenotype Analysis of Arabidopsis thaliana Based on Optimized Multi-Task Learning," Mathematics, MDPI, vol. 11(18), pages 1-18, September.
    2. Wei Liu & Shijie Tian & Qingyu Wang & Huanyu Jiang, 2023. "Key Technologies of Plug Tray Seedling Transplanters in Protected Agriculture: A Review," Agriculture, MDPI, vol. 13(8), pages 1-19, July.

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