Early prediction of the growth and development of plants is important for the intelligent breeding process, yet accurate prediction and simulation of plant phenotypes is difficult. In this work, a prediction model of plant growth and development based on spatiotemporal long short-term memory (ST-LSTM) and memory in memory network (MIM) was proposed to predict the image sequences of future growth and development including plant organs such as ears. A novel dataset of wheat growth and development was also compiled. The performance of the prediction model of plant growth and development was evaluated by calculating structural similarity index measure (SSIM), mean square error (MSE), and peak signal to noise ratio (PSNR) between the predicted and real plant images. Moreover, the optimal number of time steps and the optimal time interval between steps were determined for the proposed model on the wheat growth and development dataset. Under the optimal setting, the SSIM values surpassed 84% for all time steps. The mean of MSE values was 46.11 and the MSE values were below 68 for all time steps. The mean of PSNR values was 30.67. When the number of prediction steps was set to eight, the prediction model had the best prediction performance on the public Panicoid Phenomap-1 dataset. The SSIM values surpassed 78% for all time steps. The mean of MSE values was 77.78 and the MSE values were below 118 for all time steps. The mean of PSNR values was 29.03. The results showed a high degree of similarity between the predicted images and the real images of plant growth and development and verified the validity, reliability, and feasibility of the proposed model. The study shows the potential to provide the plant phenotyping community with an efficient tool that can perform high-throughput phenotyping and predict future plant growth.

Berry thinning is one of the most important tasks in the management of high-quality table grapes. Farmers often thin the berries per cluster to a standard number by counting. With an aging population, it is hard to find adequate skilled farmers to work during thinning season. It is urgent to design an intelligent berry-thinning machine to avoid exhaustive repetitive labor. A machine vision system that can determine the number of berries removed and locate the berries removed is a challenge for the thinning machine. A method for instance segmentation of berries and berry counting in a single bunch is proposed based on AS-SwinT. In AS-SwinT, Swin Transformer is performed as the backbone to extract the rich characteristics of grape berries. An adaptive feature fusion is introduced to the neck network to sufficiently preserve the underlying features and enhance the detection of small berries. The size of berries in the dataset is statistically analyzed to optimize the anchor scale, and Soft-NMS is used to filter the candidate frames to reduce the missed detection of densely shaded berries. Finally, the proposed method could achieve 65.7 AP^box, 95.0 $A{P}_{0.5}^{\mathit{box}}$, 57 $A{P}_s^{\mathit{box}}$, 62.8 AP^mask, 94.3 $A{P}_{0.5}^{\mathit{\text{mask}}}$, 48 $A{P}_s^{\mathit{\text{mask}}}$, which is markedly superior to Mask R-CNN, Mask Scoring R-CNN, and Cascade Mask R-CNN. Linear regressions between predicted numbers and actual numbers are also developed to verify the precision of the proposed model. RMSE and R² values are 7.13 and 0.95, respectively, which are substantially higher than other models, showing the advantage of the AS-SwinT model in berry counting estimation.

Wheat plays a crucial role in ensuring food security. However, its complex genetic structure and trait variation pose significant challenges for breeding superior varieties. In this study, a genomic prediction method for wheat (WheatGP) is proposed. WheatGP is designed to improve the phenotype prediction accuracy by modeling both additive genetic effects and epistatic genetic effects. It is primarily composed of a convolutional neural network (CNN) module and a long short-term memory (LSTM) module. The multilayer CNNs within the CNN module focus on capturing short-range dependencies within the genomic sequence. Meanwhile, the LSTM module, with its unique gating mechanism, is designed to retain long-distance dependency relationships between gene loci in the features. Therefore, WheatGP could comprehensively extract multilevel features from genomic inputs. Compared to ridge regression best linear unbiased prediction (rrBLUP), extreme gradient boosting (XGBoost), support vector regression (SVR), and deep neural network genomic prediction (DNNGP), WheatGP demonstrates a clear advantage in terms of prediction accuracy. The prediction accuracy for wheat yield reaches 0.73, while the prediction accuracies for various agronomic traits range between 0.62 and 0.78. It also exhibits robust performance across other crop types and multi-omics datasets. In addition, SHapley Additive exPlanations (SHAP) is employed to evaluate the contributions of inputs to the predictive model. As a high-performance tool for genomic prediction in wheat, WheatGP opens up new possibilities for achieving efficient and optimized wheat breeding.

An efficient and accurate estimation of wheat growth and yield is important for wheat assessment and field management. To improve the accuracy and stability of wheat growth and yield estimation, an estimation method based on a genetic algorithm-improved support vector regression (GA-SVR) algorithm was proposed in this study. The correlation analysis between vegetation indices calculated from spectral data and wheat growth phenotypes and yields was performed to obtain the optimal combination of vegetation indices with high correlation and good estimation performance. At the same time, the optimal model for wheat growth monitoring was screened and constructed in experiments with 12 wheat varieties and 3 gradient nitrogen fertilizer application levels. Then, the yield estimation model was established and its applicability was verified under different nitrogen fertilizer application levels. The results showed that the constructed models for the leaf area index, plant height, and yield estimation performed well, with coefficients of determination of 0.82, 0.71, and 0.70, and root mean square errors of 0.09, 2.7, and 68.5, respectively. This study provided an effective UAV remote sensing technique for monitoring wheat growth status and estimating yield. This study provides an effective unmanned aerial remote sensing technique for monitoring wheat growth and estimating yield, and provides technical support for wheat yield assessment and field management.