Growth visualized prediction method of multi-variety rice based on improved Pix2Pix-HD network

With the advancement of information technology and agricultural digitization, the visualized prediction of digital plant growth holds significant development potential. As an important food crop, achieving visualized growth prediction for rice is of great significance for analyzing its growth and development. However, due to the large size and complex morphology of rice, attaining high-precision and high-resolution visualized growth prediction has been challenging. This study proposes a growth prediction method for rice based on an improved Pix2Pix-HD network, achieving high-precision visualized growth prediction from the heading stage to the filling stage of rice. Comparative experiments were designed to verify the effectiveness of the model improvement scheme. The results showed that the FID, PSNR, and SSIM values between the predicted and real filling stage rice images in the test set were 24.75, 13.58, and 0.78, respectively. The average correlation coefficient between the predicted and actual phenotypes was 0.762, demonstrating good accuracy at different scales. The proposed data-driven rice growth prediction method can achieve high-resolution and high visual realism in rice growth visualization predictions, providing new solutions for rice growth forecasting.