ArticlePlant methods2025
GAN-based image prediction of maize growth across varieties and developmental stages.
Article in Plant methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundPlant growth prediction assists physiologists and botanists in analyzing future development trends, thereby shortening experimental cycles and reducing costs. Traditional growth prediction methods mainly focused on phenotypic traits instead of images, which leads to limited visual interpretability.
resultsThis article proposed a visualized growth prediction method based on an improved Pix2PixHD network, incorporating spatial attention mechanisms, an improved loss function, and a modified dropout strategy to enhance prediction accuracy and visual fidelity. The proposed method can employ maize images from early time points to predict the images of later stages. The prediction results are presented in the form of side-view growth images with a resolution of 1024 × 1024 pixels, enabling the capture of detailed, organ-level growth information. This study conducted experiments on 696 varieties, a highly genetically diverse maize population derived from the crossbreeding of 24 foundational Chinese inbred lines. The results showed that Fréchet Inception Distance, Peak Signal-to-Noise Ratio and structural similarity between the predicted images and the actual images reached 20.27, 23.23 and 0.899, respectively. The model achieved a mean Pearson correlation coefficient of 0.939 between predicted and actual phenotypic traits, while maintaining robust performance across different time intervals. It was also demonstrated that the model outperformed the existing related studies. The code is available online.
conclusionThe results showed that the method can make realistic predictions of multi-variety maize growth based on high-resolution generation. Furthermore, it can achieve prediction of maize growth throughout the entire growth cycle with high accuracy. In conclusion, this article provided a novel solution for visualized growth prediction of large plants with complex physiological structures throughout the entire growth cycle. A primary limitation of this study is its focus on modeling and predicting crop growth under uniform environmental conditions, without considering environmental variability. Future work will aim to incorporate diverse environmental factors into the model to enhance its robustness and predictive accuracy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.