TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.

A variety test platform for the standardization and data quality improvement of crop variety tests.

Variety testing is an indispensable and essential step in the process of creating new improved varieties from breeding to adoption. The performance of the varieties can be compared and evaluated based on multi-trait data from multi-location variety tests in multiple years. Although high-throughput phenotypic platforms have been used for observing some specific traits, manual phenotyping is still widely used. The efficient management of large amounts of data is still a significant problem for crop variety testing. This study reports a variety test platform (VTP) that was created to manage the whole workflow for the standardization and data quality improvement of crop variety testing. Through the VTP, the phenotype data of varieties can be integrated and reused based on standardized data elements and datasets. Moreover, the information support and automated functions for the whole testing workflow help users conduct tests efficiently through a series of functions such as test design, data acquisition and processing, and statistical analyses. The VTP has been applied to regional variety tests covering more than seven thousand locations across the whole country, and then a standardized and authoritative phenotypic database covering five crops has been generated. In addition, the VTP can be deployed on either privately or publicly available high-performance computing nodes so that test management and data analysis can be conveniently done using a web-based interface or mobile application. In this way, the system can provide variety test management services to more small and medium-sized breeding organizations, and ensures the mutual independence and security of test data. The application of VTP shows that the platform can make variety testing more efficient and can be used to generate a reliable database suitable for meta-analysis in multi-omics breeding and variety development projects.

Detection of Cadmium Risk to the Photosynthetic Performance of Hybrid Pennisetum.

Photosynthesis plays an essential role in plant growth and crop yield, and the mechanisms of the effects of cadmium (Cd) on photosynthetic performance require more attention. The acute toxicity of Cd in soil to the photosynthetic capacity of Hybrid Pennisetum was evaluated using gas exchange parameters, A/Ci curves, light response curves, and chlorophyll a fluorescence transients after exposure to elevated Cd concentrations (0, 10, 20, 50, 70, and 100 mg kg-1) for a 3-month period. The results indicated that leaf Cd concentration in Hybrid Pennisetum increased with the strength of soil Cd stress and ranged from 4.9 to 15.8 µg g-1 DW. The accumulation of leaf Cd severely restricted photosynthesis and its non-stomatal limitation in regulating the photosynthetic performance of Hybrid Pennisetum. The leaf chloroplasts at 10 and 20 mg kg-1 Cd concentrations showed no noticeable change, but the chlorophyll content significantly decreased by 9.0-20.4% at 50-100 mg kg-1 Cd concentrations. The Cd treatments also decreased plant ribulose-1,5-bisphosphate (RuBP) activity (Vcmax ) and regeneration capacity (Jmax ), triose phosphate utilization (TPU), light-saturated photosynthesis (Amax ), apparent quantum yield (AQY), light saturation point (LSP), and dark respiration (Rday ), but Cd treatment increased the light compensation point (LCP). The shape of chlorophyll a fluorescence transients in leaves was altered under different Cd treatments. The increased OJ phase and the decreased IP phase in fluorescence induction curves suggested that Cd toxicity inhibited both light use efficiency and photodamage avoidance ability. These results suggested that the decrease in photosynthesis through exposure to Cd may be a result of the decrease in leaf chlorophyll content, Rubisco activity, and RuBP regeneration, inhibition of triose phosphate utilization, reduction of the ability to use light and provide energy, and restrictions on electron transport in PSII.