Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network.

Abstract

To solve the problem of low survival rate caused by unscreened transplanting of seedlings. This study proposed a selective transplanting method of leafy vegetable seedlings based on the ResNet 18 network. Lettuce seedlings were selected as the research object, and a total of 3,388 images were obtained in the dataset. The images were randomly divided into the training set, validation set, and test set in the ratio of 6:2:2. The ResNet 18 network was used to perform transfer learning after tuning, identifying, and classifying leafy vegetable seedlings, and then establishing a model to screen leafy vegetable seedlings. The results showed that the optimal detection accuracy of the presence and health of seedlings in the training data set was above 100%, and the model loss remained at around 0.005. Nine hundred seedlings were selected for the validation test, and the screening accuracy rate was 97.44%, the precision rate of healthy seedlings was 97.56%, the recall rate was 97.34%, the precision rate of unhealthy seedlings was 92%, and the recall rate was 92.62%, which was better than the screening model based on the physical characteristics of seedlings. If they were identified as unhealthy seedlings, the manipulator would remove them during the transplanting process and perform the seedling replenishment operation to increase the survival rate of the transplanted seedlings. Moreover, the seedling image is extracted by background removal technology, so the model processing time for a single image is only 0.0129 s. This research will provide technical support for the selective transplantation of leafy vegetable seedlings.

Plant phenotyping relevance

画像とResNet18を用いて苗の存在・健全性を推定し、選別する手法自体が研究の中心であり、画像処理性能と検証結果も示されているため、植物表現型計測手法として収録する。

Code and data availability

The paper describes a paper-specific lettuce seedling image dataset (3,388 images) and trained ResNet 18 screening models, but no public repository, deposit, or authors' URL is provided. The data availability statement only promises raw data from the authors upon request, so any qualifying asset requires contacting the

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