The stable production of seedlings is very important for seedling growers. Predicting the growth of seedlings helps growers promptly adjust management strategies and production expectations. Traditional methods rely on historical growth data or assess current plant physiological parameters to estimate future growth. This study aims to predict future images directly from historical growth images of tomato seedlings. Specifically, a dataset of 10-d image sequences of tomato seedlings was collected. Then, an algorithm based on several neural networks was applied to predict the images of the next 5 d based on the images of the first 5 d. The algorithm was composed of a causal long short-term memory (LSTM) unit, a gradient highway unit (GHU), and a pix2pix unit. The experimental results showed that the introduction of a Generative Adversarial Network (GAN) further enhanced the clarity and realism of the predicted images, ensuring higher quality and more accurate visual results. From the perspective of image similarity, the average mean squared error (MSE) reached 394.97 and the average structural similarity (SSIM) reached 0.90 over 5 d. From the perspective of biological information, the average prediction errors of the plant area were 1.7, 1.4, 1.5, 0.9, and 3.2 cm² over the 5 d, and the average prediction errors of plant height were 1.7, 1.9, 4.6, 6.9, and 4.5 mm, respectively. The extracted biological information such as plant area and height showed good following performance compared with the real growth information. The research results show that predicting future plant images from historical images has the potential to become a useful tool for nursery growers to adjust management strategies and production expectations.

Agricultural activities in Japan have been limited to the aging and diminishing labor force, as well as the small plots of cultivated land in mountainous regions. Therefore, smart agriculture has been launched to ensure food security in Japan. A systematic framework has been provided to support the digitization of forestry and aquaculture, including the basic Law and national strategy. Numerous technological innovations have also involved machinery automation, information systems, rural network transformation, agricultural aviation, and plant factories. Modern agriculture has allowed the 1 404 000 core farmers and the majority of the elderly population to achieve nearly 130 million people self-sufficient in the major food. Among them, the Japan Agricultural Association has played a pivotal role in this transformation, covering the various processes at multiple levels and departments. These fundamental facilitates have provided crucial support to implement smart agriculture. This study aims to analyze the trajectory and efficacy of smart agriculture in Japan. The valuable insights were then obtained for the several suggestions. Firstly, it was very necessary to innovate the agricultural system and mechanism, in order to create the channels for the seamless circulation of information, manpower, and resources. The agricultural environment was sustained to leverage the strengths of diverse entities, such as local governments, universities, and enterprises. Long-term empirical research was performed on smart agricultural technology in rural revitalization. These insights should be adapted to the local conditions in the agricultural production, sales, promotion, and management from the Japanese Farmers' Association's experience. The findings can greatly contribute to the revitalization of rural areas in China. In turn, the trajectory of rural modernization can be accelerated to position as a formidable force in the realm of smart agriculture. These lessons can also be incorporated into sustainable and technologically advanced agricultural systems.