# Radish Growth Stage Recognition Based on GAN and Deep Transfer Learning
## 1 Introduction  
The state of agricultural development has a decisive effect on national development. Mechanization alone is insufficient. With the rapid development of computer vision technology and machine learning, especially deep learning, an increasing number of researchers are applying such technologies to the agricultural field. Different from previous methods, computer vision technology has the following advantages: low cost, good real-time data acquisition, and no contact with the tested object, thereby avoiding damage to the tested object. The ability of smart agriculture to determine the plant growth stage is of great significance to the healthy growth and high yield of crops. Plant growth stage identification would provide valuable data for the planning, organization, and timely implementation of agricultural activities (spraying, irrigation, fertilization, etc. ), thereby playing an important role in guiding such activities and predicting crop yield [1, 2, 3]. Of the many existing plant species, Cruciferase is economically valued for its variety and distribution all over the world. Numerous varieties are closely related to people's daily life as sources of food and nutrition, and their application is extensive, e.g., some parts can be used as vegetables and oil crops, while others can be made into medicinal herbs, spices, etc. Vegetables in the cruciferous family comprise two genera, one being Brassica and the other being the more common radish genus, which includes the large green radish, radish, and so on. Vegetable planting technology is mature; however, it also increases the cost of human learning. Scientific and efficient planting can generate great economic benefits and social value. Therefore, improving the economic benefits ofvegetable planting and realizing automatic management is of great significance. Scientific studies have shown that cruciferous plants have different needs in terms of illumination, nutrition, and water at different growth stages. In addition, corresponding field operations, such as thinning, topdressing, and soil raising, depending on growth state, are crucial to the quality and yield of crops. Traditional identification methods are based on manual observation. Correct identification of plant growth stages and states requires professional knowledge, and this knowledge requires time to learn and practice. At present, information on plant growth stage and status must be manually and regularly recorded from collected images, and then analyzed. Once tagged and archived, the information can then be used for the development of irrigation, fertilization, and disinfection systems, as well as yield prediction. However, this method is time consuming and costly, and the direct contact involved may cause damage to plants. In recent years, with the breakthrough of computer vision technology, image recognition methods based on deep learning have become widely used. Image recognition technology is not only low cost but also convenient for real-time analysis. Radish genera share similar growth characteristics, with rosette basal leaves and alternate stems and leaves, so their morphologies at different growth stages are very similar. We can use this characteristic to realize the identification of the growth stages of radish plants and develop different auxiliary and automatic planting strategies according to the different growth stages, thereby promoting the development of smart agriculture. A convolutional neural network [4] (CNN) automatically extracts the important features in a picture, which saves labor costs. However, the study of plant growth stage identification and growth state monitoring is not complete. This is because few datasets exist for each growth stage of plants; moreover, a small dataset would lead to a complex convolutional neural network overfitting, making it difficult to achieve improved recognition effects.