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

Shengyong Xu, Yi Zhang, Wanjing Dong, Zhilong Bie, Chengli Peng and Yuan Huang ()
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Shengyong Xu: College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Yi Zhang: College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Wanjing Dong: College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Zhilong Bie: College of Horticulture and Forestry Sciences, 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

Agriculture, 2023, vol. 13, issue 1, 1-19

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.

Keywords: strong seedling model; phenotype measurement; machine learning; grow prediction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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