# Classification Method of Plug Seedlings Based on Transfer Learning
## 1 Introduction  
In greenhouse plug seedling cultivation, it is necessary to replant unqualified seedlings or cells without seedlings in plug trays, to improve the utilization of the trays and promote the unified mechanization of greenhouse seedlings. The classification of unqualified seedlings, qualified seedlings and lack of seedlings during the replanting process is an important exercise. At present, relevant researchers use image segmentation to measure the leaf area or other features of seedlings to identify unqualified seedlings and lack of seedlings. [PERSON] et al. used tomato seedlings as test samples and applied a morphological-based watershed algorithm to complete leaf edge segmentation, extracting each leaf area, and leaf circumference of the seedlings in a plug tray was used to identify the seedlings; recognition accuracy reached 98% [1]. [PERSON] et al. aimed at the leaf overlap of the plug seedlings, proposed a decision-making method combining the central information of the leaflet area and improved the watershed method to segment the seedlings, and then calculated the seedling leaf area to realize the seedling identification judgment; the recognition accuracy rate was 95% [2, 3]. [PERSON] et al. usedstructured light and industrial camera methods to identify seedlings by detecting leaf area and stem height, and the recognition accuracy was over 90% [4]. [PERSON] et al. extracted the measurements of scion and rootstock stem diameters to grade scion and rootstock seedlings [5]. [PERSON] et al. compared the pixel value of seedling with threshold value to determine whether the cell was short of seedlings according to the binary images of the plug tray. The accuracy rate of judging the null plug cells and the seedling cells with the Arabidopsis plug seedlings of 25- and 35-days were all 100% [6]. In the process of seedlings classification with the traditional image processing method, the features used for the depiction of information will be more or less lost in the image process; the artificial extraction of certain feature algorithms is complex, and the recognition accuracy depends largely on the judgment of the selected features. In the process of image segmentation and feature extraction of the plug seedlings, the classification accuracy of the sample image is easily reduced by the influence of uncertain factors, such as changes in the light intensity of the working environment, differences in the matrix of the plug tray, and differences in the growth morphology of the plug seedlings. The robustness of this method for classifying seedlings needs to be further improved. Convolutional neural network is a robust method for classification [7; 8; 9], which has been widely verified in the fields of agricultural product classification and agricultural disease identification [10; 11; 12; 13]. Convolutional neural network method has the advantage that the seedlings image can be directly used as the input of the network, avoiding the complicated feature extraction and data reconstruction process in the traditional recognition algorithm. In addition, with the data augmentation before training the network, this method has strong robustness in classification. Transfer learning is a machine learning method based on pre-training model, which can effectively solve the problem of small training sample size [14; 15; 16; 17]. Due to the limited number of samples of plug seedlings in different types of plug trays and the high cost of sample acquisition, this paper used a transfer learning method based on convolutional neural network to study the classification of unqualified seedlings, qualified seedlings and lack of seedlings. The plug seedlings classification method proposed in this paper uses pepper seedlings as the test object, and divides qualified seedlings and unqualified seedlings according to the growth characteristics of pepper seedlings. First, image processing was used to identify the plug tray specification before seedling classification.