Abstract
Crop breeding is one of the main approaches to increase crop yield and improve crop quality. However, the breeding process faces challenges such as complex data, difficulties in data acquisition, and low prediction accuracy, resulting in low breeding efficiency and long cycle. Deep learning-based crop breeding is a strategy that applies deep learning techniques to improve and optimize the breeding process, leading to accelerated crop improvement, enhanced breeding efficiency, and the development of higher-yielding, more adaptive, and disease-resistant varieties for agricultural production. This perspective briefly discusses the mechanisms, key applications, and impact of deep learning in crop breeding. We also highlight the current challenges associated with this topic and provide insights into its future application prospects.
Introduction
Crop quality has always been a focal point in human cultivation, and crop breeding, as a primary approach to increasing crop yield and improving crop quality, is one of the oldest agricultural activities, equivalent to human civilization (). Emerging as the times require, crop breeding is the process of artificially selecting and cultivating plant varieties to improve their agronomic traits and economic benefits (). In the early days, farmers preserved and planted the seeds of the best-performing plants to grow crops in the next season, a natural selection process that facilitated the accumulation of favorable traits (). Over time, people gradually realized the importance of specific traits for crop yield, quality, disease resistance, and adaptability, and began consciously to select and breed plants with these characteristics. With the development of technology and improvement of living standards, higher demands have been placed on crop yield and quality, necessitating continuous innovation in breeding techniques, methods, and applications to provide strong support (; ). This has significant impacts and effects on agriculture and the economy, promoting sustainable agricultural development.
During the entire history of crop breeding technology, it has roughly gone through three major stages, and it is now advancing towards the fourth stage. The first stage is conventional breeding (Breeding 1.0), which mainly relies on visual observation of crop phenotypes and subjective selection of crops that meet predetermined requirements. Generally, wild species are gradually domesticated into cultivated varieties with improved qualities through multiple rounds of artificial selection (; ). However, this stage primarily relies on natural variation and the subjective experience of breeders, resulting in slow progress, low efficiency, and high uncertainty. In the late 19th century, with the rapid development of genetics, genetic breeding (Breeding 2.0) emerged as the mainstream, bringing breeding into the realm of science. During this stage, significant success was achieved in crop breeding for crops like wheat, rice, maize, greatly improving yields. Unfortunately, there were still shortcomings such as long breeding cycles, low efficiency in genetic improvement, and high field costs (; ). At the end of the 20th century, genetic engineering propelled the development of modern molecular biology, ushering in the era of molecular breeding (Breeding 3.0). The gradual application of technologies such as transgenic techniques, molecular markers, genomic selection, and gene editing provided more efficient, precise, and targeted breeding methods. Nevertheless, high costs and complexity remain limiting factors for the application of molecular breeding (). Breeding scientists sincerely hope that the integration of new generation information technologies such as big data and artificial intelligence with biotechnology will propel crop breeding into the era of Breeding 4.0, which also terms as Intelligent breeding () and is marked by Deep Learning-Empowered Crop Breeding (; ).
With the push of large-scale datasets, powerful computing capabilities, and algorithmic improvements, deep learning has made breakthrough progress in multiple fields (). Deep learning is a machine learning method that revolves around the idea of building multi-layer neural network models to simulate the neural networks of the human brain, enabling the learning and pattern recognition abilities of data, which can be further applied to tasks such as classification, prediction, and generation (). Depending on whether the training data has label information, deep learning can be divided into two learning modes (): (1) Supervised Learning: It relies on labeled training data, where labels correspond to the expected outputs or categories for each input sample. In this mode, explicit supervision signals are provided to the model, enabling it to learn the mapping relationship between inputs and outputs. The algorithms include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Convolutional Neural Networks (GCNs) (). Neural networks are models used to capture nonlinear dependencies. They transform inputs through hidden layers, mapping them to a space where classes can be linearly separated. In the example of splice site classification, a singlelayer neural network employs logistic regression for prediction but fails to accurately differentiate spliced and unspliced data points. Surprisingly, by utilizing neural networks with intermediate layers, more complex nonlinear transformations can be performed, enabling effective discrimination between spliced and unspliced data points (Figure 1A). Deep neural networks, on the other hand, are neural network architectures that consist of multiple hidden layers (). (2) Unsupervised Learning: It utilizes unlabeled training data. In this case, no explicit output or category information is given, and the goal is to discover hidden structures, patterns, or features from the data through the model’s own learning process (). Unsupervised learning is commonly used for tasks such as clustering, dimensionality reduction, anomaly detection, and generative modeling (). The algorithms include Autoencoders, Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs). An autoencoder consists of an encoder and a decoder, used for data compression and reconstruction (). The encoder compresses input data into lower dimensions and stores it in the bottleneck layer, while the decoder attempts to reconstruct the original input from the compressed data in the bottleneck layer. A generative adversarial network consists of a generator and a discriminator, trained together to generate realistic samples and perform discrimination (). The discriminator is responsible for distinguishing between real and synthetic data, while the generator aims to deceive the discriminator by generating more realistic synthetic samples (Figure 1B).
Figure 1
Deep learning-empowered breeding is a method that applies deep learning techniques to improve and optimize the breeding process. It utilizes deep learning models to analyze and process agricultural and genetic data, in order to predict and optimize the agricultural characteristics and genetic traits of crops (). Deep learning-assisted breeding can enhance breeding efficiency, accelerate the improvement process of crops or animals, and provide higher-yielding, more adaptive, and disease-resistant varieties for agricultural production, through steps such as data collection and preprocessing, model construction and training, as well as genetic parameter optimization and selection (). The aim of this perspective is to provide an overview of the latest developments in deep learning in the field of crop breeding, analyze current challenges, and highlight its potential as a promising technology for crop breeding.
Principles of deep learning-empowered crop breeding
Deep learning solves complex problems by processing large-scale data. Currently, images remain the main data format for phenotypic selection in crop breeding (). The application of deep learning in plant phenotyping image processing is rapidly advancing, especially with the impressive performance of CNN in analyzing phenotype big data (). It possesses powerful feature extraction and modeling capabilities, providing new approaches for overcoming challenges in data analysis. The workflow for crop breeding based on deep learning generally includes six steps: (1) Data collection: Gather agricultural and genetic data relevant to the target crops, including phenotypic traits, environmental factors, genetic markers, and other related information (). (2) Data preprocessing: Clean and preprocess the collected data, involving data normalization, feature extraction, missing data handling, and data augmentation techniques (). (3) Model construction: Build a deep learning model suitable for the specific breeding task, selecting appropriate neural network architectures based on the data nature and breeding objectives. (4) Model training: Train the deep learning model using preprocessed data, optimizing model parameters to minimize differences between predicted outputs and observed values. Training typically involves techniques like backpropagation and gradient descent to update model weights (). (5) Genetic parameter optimization and selection: Utilize the trained deep learning model to predict and evaluate agricultural characteristics and genetic traits of crops. Optimize genetic parameters and select suitable individuals for further breeding based on the predictions. (6) Iterative improvement: Repeat the training, evaluation, and selection steps, iteratively improving the deep learning model and breeding process (). This establishes an effective breeding plan, enabling the offspring of parental generations to approach the desired phenotypes.
Deep learning-empowered breeding is built upon important identified genes, integrating multiomics, next-generation biotechnologies, and novel information technologies such as artificial intelligence and big data. The identification of important genes using genetics and transgenic methods forms the foundation of deep learning-based breeding. New technologies, including multiomics, artificial intelligence, and big data, expedite the breeding process through plant phenotypic analysis and high-throughput phenotyping platforms, facilitate the evaluation of plant materials, discovery of specific genes, and accelerated breeding (; ). Integration of multiomics data, encompassing genomics, metabolomics, phenomics, proteomics, and transcriptomics, aids in analyzing biological changes and regulatory processes, identifying key genes and regulatory elements, and driving plant breeding (). What’s even more exciting is that Telomere-to-Telomere (T2T) complete genome and T2T whole-genome analysis serve as representative markers for accurately identifying genetic diversity and enhancing functional genomics and genetic improvement (). Additionally, gene editing techniques have also contributed to breeding advancements (). Intelligent breeding strategies driven by big data and artificial intelligence, enable targeted breeding, such as through comprehensive genomic and environmental prediction (iGEP) based on genomics and population-environment interactions (). Deep learning frameworks support automatic differentiation, enabling efficient implementation of these scores with just a few lines of code. They will assist in handling extensive multidimensional big data of genotype-phenotype-environment, facilitating efficient selection and cultivation of high-quality, disease-resistant new varieties (see Table 1). It should be pointed out that extensive genetic experiments with correlated phenotypic and environmental data are necessary (; ). It is also important, especially in complex models, to indirectly examine parameters by inspecting the input-output relationships for each predicted example. Feature importance scores highlight the most influential parts of a given input for model predictions, helping to explain why such predictions are made. In DNA sequence-based models, feature importance scores highlight sequence motifs and are widely used in genomics (; ; ). They can also be used to explore more complex epistatic interactions (). Feature importance scores can be divided into two categories: perturbation-based and backpropagation-based (Figure 2). The former perturbs input features and observe changes in the output, but it is computationally expensive. On the other hand, the latter calculates the importance scores for all input features using a single backpropagation pass, making them computationally efficient. The simplest backpropagation-based importance scores are saliency maps () and input-masked gradients ().
Table 1
| Categories (model) | Data | Application | |
|---|---|---|---|
| CNN (DeepBind) | DNA sequences | DNA and Gene Characteristics () | |
| CNN (DeepSEA) | DNA sequences | DNA and Gene Characteristics () | |
| Bi-GRU and LSTM (DeepCpG) | DNA sequences | DNA and Gene Characteristics () | |
| CNN and RNN (BiRen) | DNA sequences | DNA and Gene Characteristics () | |
| CNN (Basset) | DNA sequences | DNA and Gene Characteristics () | |
| RNN | DNA sequences, DNA probes | DNA and Gene Characteristics () | |
| ResNet and LSTM (DeepcycP) | DNA sequences | DNA and Gene Characteristics () | |
| DBN | Gene sequences | DNA and Gene Characteristics () | |
| GCN | Genetic diagram | interaction | DNA and Gene Characteristics () |
| CNN and LSTM (DeepNovo) | Peptide spectrum | sequences, | Protein Characteristics () |
| BiLSTM and SVM-rank (pNovo3) | Peptide spectrum | sequences, | Protein Characteristics () |
| GRU | spectrum | Protein Characteristics () | |
| Stacked AutoEncoder and DNN | protein sequences | Protein Characteristics () | |
| RCNN | protein sequences | Protein Characteristics () | |
| Evoformer and transformer (AlphaFold) | protein sequences | Protein Characteristics () | |
| GCN (Decagon) | protein-protein interaction network | Protein Characteristics () | |
| GAN | DNA sequences | Protein Characteristics () | |
| Multilayer Perceptron and autoencoder | SNP | Genomics Variations () | |
| GAN | RNA sequences | Genomics Variations () | |
| DCNN | DNA sequences | Genomics Variations () | |
| CNN (AMBER) | gene sequences | Genomics Variations () | |
| RCNN | RGB image | Genomics Variations () | |
| Multilayer Perceptron Classifier | DNA sequences | Genomics Variations () | |
Deep learning algorithms and models used in intelligent breeding.
Figure 2
Applications of deep learning in crop breeding
DNA and gene characteristics
The shape of DNA plays an important role in the specificity of transcription factor (TF)-DNA binding (), and deep learning models can utilize various types of data for analysis (). Understanding the sequence specificity of DNA and RNA binding proteins is crucial for biological system regulation models and pathogenic variant identification (). There are currently several deep learning-based methods for predicting TF binding properties. DeepBind (), Basset (), and DeepSEA () were among the earliest convolutional neural networks (CNNs) applied to genomic data analysis. DeepBind trained multiple single-task models to predict the binding affinities of transcription factors, while DeepSEA compiled a large set of chromatin maps for non-coding variants to study chromatin features, and Basset predicted DNA accessibility features. The impact of functional non-coding variants was evaluated in DeepSEA, DFIM (), and DeFine (). This has always been considered a challenge to identify critical genomic regulatory regions in species with abundant repetitive elements and broad intergenic regions. To address this challenge, efficient and accurate annotation of regulatory regions in maize was achieved using methods based on natural language processing, such as k-mer grammar (). These methods have played an important role in the prediction of functional non-coding variants, regulatory region annotation, and transcription factor binding site (TFBS) prediction. Machine learning models have proven to be efficient in plant biology, capable of being trained on various types of sequencing data while incorporating additional information, such as DNase I hypersensitivity data, to improve the prediction of in vivo transcription factor binding sites (). In summary, CNNs have been widely applied in predicting molecular phenotypes from DNA sequences and have become advanced models. They have been used for classifying transcription factor binding sites (), chromatin function (), DNA contact mapping (), DNA methylation (; ), gene expression (), and RNA binding protein (). Additionally, CNNs have been successfully applied to tasks such as RNA specificity prediction () and enhancing Hi-C data resolution (). Not surprisingly, CNNs can model long-range dependencies in the genome and improve the accuracy of predicting molecular phenotypes from linear DNA sequences through dilated convolutions (). Interestingly, in addition to the CNN model, several other deep learning models are also used to analyze genetic characteristics. For instance, designed the DeepCpG model based on RNN, which can predict single-cell methylation states from local DNA sequences and observed neighboring methylation states. constructed a deep learning model to predict the depth of next generation sequencing according to the DNA probe sequences. Enhancer elements are non coding fragments of DNA that play a crucial role in controlling gene expression programs. proposed a hybrid BiRen architecture based deep learning, which only used DNA sequences to predict enhancers. constructed a deep model called DeepcycP that combines the Inception ResNet structure and LSTM layer, which can predict intrinsic DNA cyclization with high fidelity. designed a deep learning algorithm DBN, which used whole-genome single-nucleotide polymorphism (SNP) as training and testing data to construct a genome prediction model. The results showed that the DBN algorithm had a correlation of 0.579 within the range of [−1,1] with non additive features. studied gene expression by deep learning and applied bias to the model using gene interaction maps, which has advantages in specific tasks within a low data range.
Protein characteristics
There is a close relationship between the function and structure of proteins. The function of a protein is determined by its tertiary structure, which can be revealed through comprehensive analysis of various protein characteristics. To extract important amino acid features from primary peptide sequences, DeepNovo () was developed using the CNN method. pNovo3 () utilizes a learning-to-rank framework to differentiate similar peptide candidates for each spectrum. It employs three metrics to measure the similarity between experimental and theoretical spectra, with the theoretical spectra precisely predicted through deep learning using the pDeep algorithm. In mass spectrometry-based proteomics, identification and quantification of peptides and proteins heavily rely on database searching and spectrum matching. The lack of accurate models for predicting fragment ion intensities limits the practicality of these methods. By expanding the ProteomeTools synthetic peptide library and training the deep neural network Prosit, the prediction accuracy of chromatographic retention time and fragment ion intensities has been significantly improved (). used GAN to generate DNA sequences for proteins with variable coding lengths, which have ideal biophysical properties. Protein-protein interactions are crucial for understanding biological processes and disease mechanisms. Researchers have explored various methods to predict protein-protein interactions, including sequence-based prediction techniques () and deep learning models (). One approach involves unsupervised derivation of novel protein features from the “proteinprotein” interaction network, followed by using these features to predict protein functions in different tissues (). proposed the graph convolutional neural network model Decagon, and used this model to construct multimodal graphs of protein protein interactions, drug protein target interactions, and multi drug side effects.Some of these methods also incorporate physicochemical features of proteins and topological features of protein-protein interaction (PPI) networks to enhance predictive performance through multimodal supervised deep representation learning (). A novel residue representation method called Res2vec has been designed to represent protein sequences, combining effective feature embedding and powerful deep learning techniques, providing a universal computational pipeline for inferring “protein-protein” interactions (). The confidence score of a protein sequence pair can be regarded as a measure of PPI. Therefore, a deep learning framework (), namely the ordinal regression and recursive convolutional neural network approach, has been introduced to predict PPI from the perspective of confidence. Analysis of co-variation in homologous sequences aids in predicting protein structures. AlphaFold is an algorithm that predicts protein structures using deep learning methods, training neural networks to predict distances between residues and generate protein structures (). AlphaFold2 is an improved version of AlphaFold, greatly enhancing the accuracy of protein structure prediction by introducing new neural network architectures and training procedures ().
Genomics variations
Despite the presence of numerous genetic variations in natural populations, it is possible to train deep learning models on a small subset of these variations to predict the effects across the entire spectrum of mutations (). For instance, models trained on certain genes can be used to predict the outcomes of other genes. These models encompass various types of mutations, including common alleles as well as rare and low-frequency variants, regardless of their impact on gene function. constructed a deep automatic encoder model to evaluate the impact of genetic variation on gene expression changes. developed an image-based wheat spike counter using the Faster R-CNN algorithm, revealing significant differences between genotypes. ExPecto is a deep learning framework that accurately predicts the transcriptional effects of mutations in DNA sequences, including rare or unobserved mutations (). This framework enables initial predictions for exploring the evolutionary constraints on gene expression and the effects of mutational diseases. Furthermore, models trained in one species can be directly applied to closely related species (), due to the conservation of molecular processes in closely related species. proposed an unsupervised clustering method and developed a deep learning model accordingly to predict gene mutations. used GAN to simulate gene expression and predict perturbations in single cells, thereby identifying biological state determining genes and ultimately inferring gene regulatory relationships. A biologically-informed automated modeling framework, known as AMBER (), has been proposed. It is a fully automated framework that efficiently designs and applies CNNs to genomic sequences. AMBER utilizes state-of-the-art neural architecture search to design optimal models for specified biological questions. Applying AMBER to modeling tasks of genomic regulatory features has demonstrated significantly more accurate predictions compared to non-neural architecture search baseline models, matching or even surpassing expert-designed models. In summary, deep learning models have the potential to greatly advance our understanding of genomic variations in relation to the ultimate phenotypes.
The impact on crop quality and yield of deep learning-empowered crop breeding
Climate change is seriously hindering the development of agricultural productivity globally, with significant impacts on crop yield and quality (). Analyzing and identifying crop images using deep learning models can aid in rapidly identifying superior plants with target traits, thereby accelerating the process of crop breeding and selecting high-yielding, disease-resistant, and other desirable varieties. Deep learning models can also recognize crop performance under adverse environmental conditions such as drought and salinity stress, helping to cultivate more resilient crop varieties (). Specifically, the identification and classification algorithm for corn leaf blight achieved high accuracy using the CNN algorithm, which is of significant importance for rapid detection of crop diseases and improving crop yields (). The solutions for crop disease identification and diagnosis were provided using different deep learning methods (). In terms of abiotic stress, the extraction of time-series chlorophyll fluorescence features using the SAE neural network algorithm provided an effective means for identifying chlorophyll fluorescence fingerprints in Arabidopsis thaliana, offering new insights for improving crop drought resistance (). By using a large number of soybean leaf images for deep learning classification, the identification and classification of soybean symptoms under non-biological stress was achieved, enabling rapid detection of non-biological stress in soybeans (). The good correlation between the classification of corn freeze damage based on spectral features of multiple genotypes and the discrimination results based on chemical values was demonstrated using a CNN model (). Employing various deep learning methods for diagnosing pumpkin leaf diseases helps farmers detect crop damage early (). Using an integrated classifier based on a deep convolutional neural network for identifying citrus pests has effectively enhanced the quality and yield of citrus fruits (). Developing a model to estimate the number of leaves and plant age for watermelon plants, and classifying them under normal and low-temperature stress, facilitates growth monitoring and improves water and sugar content in watermelons (). Training deep learning models to classify coffee leaf images and determine if they are infected with leaf rust disease can aid in early detection of diseases and enables timely measures to protect coffee crop yields and quality (). Furthermore, deep learning can analyze the correlation between phenotypic genomic, facilitating precise selection and optimization of genomic combinations as well as gene editing techniques to improve crop yields and quality. For instance, deep learning-based genomic selection models (GS) have shown outstanding performance in predicting wheat terminal quality traits, advancing the deployment of superior genotypes into broader grain yield trials (). Therefore, deep learning can analyze massive amounts of data, build intelligent breeding decisions, and rapidly create superior inbred lines, effectively shortening breeding cycles, improving breeding efficiency, reducing costs, and enhancing crop yields and quality.
Challenges and prospects
In the past 20 years, machine learning has achieved significant success in the field of agriculture. In recent years, deep learning, as a branch of machine learning, has represented the most advanced technology in smart agriculture (). As an integral part of agriculture, deep learning has been widely applied to various plant phenomics, such as image classification (), object detection (), and semantic segmentation (). Consequently, it has tremendous potential in predicting plant growth, estimating yield, detecting maturity, and perceiving biotic/abiotic stresses. However, deep learning algorithms require a large amount of labeled data (), and data acquisition comes at a high cost, especially when identifying numerous categories () or subtle differences between categories (). Furthermore, collecting phenotype data faces additional obstacles of severe occlusion and various lighting conditions (), which increase the time required for obtaining the necessary annotations. Genotypic, phenotypic, and environmental big data form the core of intelligent breeding design (). Nevertheless, there is a significant shortage of accumulated phenotype data, and the problems with traditional manual detection are increasingly prominent, necessitating a balanced consideration of accuracy, throughput, and cost (). It is anticipated that breakthroughs and innovations in next-generation sensors and robotics will serve as underlying driving technologies to accelerate the acquisition of crop phenotype big data (). By utilizing bio-sensors and agricultural robots, continuous detection of multiple traits is achieved, leading to improved detection accuracy, but the development of sensors and robots also faces certain challenges. Firstly, the working mechanisms and conditions may vary significantly. Even within the same species and variety, robot components may require adjustments or replacements (), reducing the universality of robots. Most studies only report simulations, experiments, preliminary results, and specifications related to hardware/software design. In contrast, only a few studies discuss commercial solutions (). Additionally, the efficiency of the sensors and robots used is not high. If local labor is inexpensive, there is an unacceptable risk associated with using sensors and robots. Currently, a better solution may be to enable collaboration between workers and robots ().
The availability of massive big data enables informed decision-making, however the adaptability of deep learning models across different crops and environmental conditions may be limited (). Due to the differences in crop genetics and environmental factors, model transfer from one crop to another may require additional adjustments and optimization (). This poses challenges for the application of deep learning in intelligent analysis and interpretation. Big data includes plant phenotypes, genetic genotypes, environmental parameters, diseases, pest conditions, and more. What’s frustrating is that, the acquisition and processing of plant data lag behind research needs, limiting the development of intelligent breeding and functional plant genomics. Furthermore, they are not organically integrated, which hinders informed decision-making. In the future, researchers should collectively strive to establish a large-scale database, and interdisciplinary collaboration and data sharing can unlock greater potential for deep learning in breeding applications, benefiting more people from big data (). In addition, transfer learning () and few-shot learning () will be effective approaches to alleviate the deep learning models’ dependency on massive datasets. Transfer learning aims to transfer knowledge accumulated from a source task with ample labeled data to a new or similar target task, particularly when training data is limited. Notably, when the source and target domains exhibit strong similarity, transfer learning can provide a more economical and expedited solution to address the constraints of scarce training data (). The most distinct characteristic of few-shot learning is its capacity for “learning to learn”, achieved by emulating human-level concept learning, meaning that acquiring new concepts requires only a small number of labeled examples (). Approaches like data augmentation (), image segmentation (), and attention mechanisms () can be used to solve the problem of severe occlusion in collected phenotype data, and improve the performance of deep learning models when facing such problems. Deep reinforcement learning is the process of making intelligent decisions through reinforcement learning on the basis of deep learning (). By using deep reinforcement learning to plan the robot’s path and make decisions on its actions during its journey, the robot can efficiently assist farmers in crop data collection, crop picking, transportation, watering, and fertilization operations. No doubt, addressing key issues related to accurate collection, intelligent analysis of crop deep phenotype, and intelligent decision-making for precision agriculture on this basis will be of significant importance to the research of intelligent breeding.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.
Ethics statement
Written informed consent was obtained from the individual(s) for the publication of any identifiable images or data included in this article.
Author contributions
XW: Formal Analysis, Software, Visualization, Writing – original draft. HZ: Formal Analysis, Software, Visualization, Writing – original draft. LL: Visualization, Writing – original draft. YH: Visualization, Writing – original draft. HL: Conceptualization, Methodology, Supervision, Visualization, Writing – original draft. YQ: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing.
Funding
The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Key Research and Development Program of China (2022YFD2301100 and 2019YFD1000503), the Special Fund for Science and Technology Innovation of Fujian Agriculture and Forestry University (CXZX2020081A), and the Agriculture Research System of China (CARS-17).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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References
1
AbdallahN. A.PrakashC. S.McHughenA. G. (2015). Genome editing for crop improvement: challenges and opportunities. GM Crops & Food6, 183–205.
2
AbdullahiH. S.SheriffR.MahieddineF. (2017). “Convolution neural network in precision agriculture for plant image recognition and classification,” in 2017 Seventh International Conference on Innovative Computing Technology (INTECH), Piscataway, New Jersey. Vol. 10. 256–272 (Ieee).
3
AichS.StavnessI. (2017). “Leaf counting with deep convolutional and deconvolutional networks,” in Proceedings of the IEEE international conference on computer vision workshops. (Piscataway: IEEE), 2080–2089.
4
AlipanahiB.DelongA.WeirauchM. T.FreyB. J. (2015). Predicting the sequence specificities of dna-and rna-binding proteins by deep learning. Nat. Biotechnol.33, 831–838. doi: 10.1038/nbt.3300
5
AngermuellerC.LeeH. J.ReikW.StegleO. (2017). Deepcpg: accurate prediction of single-cell dna methylation states using deep learning. Genome Biol.18, 1–13. doi: 10.1186/s13059-017-1189-z
6
ArausJ. L.CairnsJ. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci.19, 52–61. doi: 10.1016/j.tplants.2013.09.008
7
BagagioloG.MatrangaG.CavalloE.PampuroN. (2022). Greenhouse robots: Ultimate solutions to improve automation in protected cropping systemsa˛ła review. Sustainability14, 6436. doi: 10.3390/su14116436
8
BanerjeeB. P.JoshiS.Thoday-KennedyE.PasamR. K.TibbitsJ.HaydenM.et al. (2020). Highthroughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response. J. Exp. Bot.71, 4604–4615. doi: 10.1093/jxb/eraa143
9
BankD.KoenigsteinN.GiryesR. (2020). Autoencoders. arXiv preprint arXiv. 2003.05991, 353–374. 2003.05991. doi: 10.1007/978-3-031-24628-9_16
10
BragançaS.CostaE.CastellucciI.ArezesP. M. (2019). A brief overview of the use of collaborative robots in industry 4.0: Human role and safety. Occup. Environ. Saf. Health202, 641–650. doi: 10.1007/978-3-030-14730-3_68
11
ChangL.DengX.-M.ZhouM.-Q.WuZ.-K.YuanY.YangS.et al. (2016). Convolutional neural networks in image understanding. Acta Automatica Sin.42, 1300–1312. doi: 10.16383/j.aas.2016.c150800
12
ChenZ.LiX.YangM.ZhangH.XuX. S. (2023). Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering. J. Pathol.: Clin. Res.9, 3–17. doi: 10.1002/cjp2.302
13
ChenW.-Y.LiuY.-C.KiraZ.WangY.-C. F.HuangJ.-B. (2019). A closer look at few-shot classification. arXiv preprint arXiv. 1904.04232, 1–17. doi: 10.48550/arXiv.1904.04232
14
CordtsM.OmranM.RamosS.RehfeldT.EnzweilerM.BenensonR.et al. (2016). “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE conference on computer vision and pattern recognition. (Los Alamitos, California: IEEE Computer Society), 3213–3223.
15
CreswellA.WhiteT.DumoulinV.ArulkumaranK.SenguptaB.BharathA. A. (2018). Generative adversarial networks: An overview. IEEE Signal Process. mag.35, 53–65. doi: 10.1109/MSP.2017.2765202
16
CrossaJ.de Los CamposG.PérezP.GianolaD.BurguenoJ.ArausJ. L.et al. (2010). Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics186, 713–724. doi: 10.1534/genetics.110.118521
17
CunninghamP.CordM.DelanyS. J. (2008). “Supervised learning,” in Machine learning techniques for multimedia: case studies on organization and retrieval (Berlin: Springer), 21–49.
18
DengY.LiuS.ZhangY.TanJ.LiX.ChuX.et al. (2022). A telomere-to-telomere gap-free reference genome of watermelon and its mutation library provide important resources for gene discovery and breeding. Mol. Plant15, 1268–1284. doi: 10.1016/j.molp.2022.06.010
19
De PreterA.AnthonisJ.De BaerdemaekerJ. (2018). Development of a robot for harvesting strawberries. IFAC-PapersOnLine51, 14–19. doi: 10.1016/j.ifacol.2018.08.054
20
DutilF.CohenJ. P.WeissM.DerevyankoG.BengioY. (2018). Towards gene expression convolutions using gene interaction graphs. arXiv preprint arXiv. 1806.06975, 1–5. doi: 10.48550/arXiv.1806.06975
21
EraslanG.AvsecŽ.GagneurJ.TheisF. J. (2019). Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet.20, 389–403. doi: 10.1038/s41576-019-0122-6
22
FanH.ZhangF.WangR.XiL.LiZ. (2020). “Correlation-aware deep generative model for unsupervised anomaly detection,” in Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, Proceedings, Part II 24. (Cham: Springer), 688–700.
23
FerentinosK. P. (2018). Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric.145, 311–318. doi: 10.1016/j.compag.2018.01.009
24
GessulatS.SchmidtT.ZolgD. P.SamarasP.SchnatbaumK.ZerweckJ.et al. (2019). Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat. Methods16, 509–518. doi: 10.1038/s41592-019-0426-7
25
GhahramaniA.WattF. M.LuscombeN. M. (2018). Generative adversarial networks simulate gene expression and predict perturbations in single cells. BioRxiv, 262501. doi: 10.1101/262501
26
GhaziM. M.YanikogluB.AptoulaE. (2017). Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing235, 228–235. doi: 10.1016/j.neucom.2017.01.018
27
GhosalS.BlystoneD.SinghA. K.GanapathysubramanianB.SinghA.SarkarS. (2018). An explainable deep machine vision framework for plant stress phenotyping. Proc. Natl. Acad. Sci.115, 4613–4618. doi: 10.1073/pnas.1716999115
28
GhosalS.ZhengB.ChapmanS. C.PotgieterA. B.JordanD. R.WangX.et al. (2019). A weakly supervised deep learning framework for sorghum head detection and counting. Plant Phenom. 1–14. doi: 10.34133/2019/1525874
29
GreensideP.ShimkoT.FordyceP.KundajeA. (2018). Discovering epistatic feature interactions from neural network models of regulatory dna sequences. Bioinformatics34, i629–i637. doi: 10.1093/bioinformatics/bty575
30
GuptaA.ZouJ. (2018). Feedback gan (fbgan) for dna: A novel feedback-loop architecture for optimizing protein functions. arXiv preprint arXiv. 1804.01694, 1–15. doi: 10.48550/arXiv.1804.01694
31
HashemifarS.NeyshaburB.KhanA. A.XuJ. (2018). Predicting protein–protein interactions through sequence-based deep learning. Bioinformatics34, i802–i810. doi: 10.1093/bioinformatics/bty573
32
HastieT.TibshiraniR.FriedmanJ.HastieT.TibshiraniR.FriedmanJ. (2009). “Unsupervised learning,” in The elements of statistical learning: Data mining, inference, and prediction (New York: Springer), 485–585.
33
HerathH. N.RafiiM.IsmailS.JJN.RamleeS. (2021). Improvement of important economic traits in chilli through heterosis breeding: a review. J. Hortic. Sci. Biotechnol.96, 14–23. doi: 10.1080/14620316.2020.1780162
34
IbeC. N. (2022). Democratizing plant genomics to accelerate global food production. Nat. Genet.54, 911–913. doi: 10.1038/s41588-022-01122-y
35
JiangS.ChengQ.YanJ.FuR.WangX. (2020). Genome optimization for improvement of maize breeding. Theor. Appl. Genet.133, 1491–1502. doi: 10.1007/s00122-019-03493-z
36
JingH.TianZ.ChongK.LiJ. (2021). Progress and perspective of molecular design breeding. Sci. China Life Sci.51, 1356. doi: 10.1360/SSV-2021-0214
37
JumperJ.EvansR.PritzelA.GreenT.FigurnovM.RonnebergerO.et al. (2021). Highly accurate protein structure prediction with alphafold. Nature596, 583–589. doi: 10.1038/s41586-021-03819-2
38
KamilarisA.Prenafeta-BoldúF. X. (2018). Deep learning in agriculture: A survey. Comput. Electron. Agric.147, 70–90. doi: 10.1016/j.compag.2018.02.016
39
KelleyD. R. (2020). Cross-species regulatory sequence activity prediction. PLoScomputationalbiology16, e1008050. doi: 10.1371/journal.pcbi.1008050
40
KelleyD. R.ReshefY. A.BileschiM.BelangerD.McLeanC. Y.SnoekJ. (2018). Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Res.28, 739–750. doi: 10.1101/gr.227819.117
41
KelleyD. R.SnoekJ.RinnJ. L. (2016). Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res.26, 990–999. doi: 10.1101/gr.200535.115
42
KhakiS.WangL. (2019). Crop yield prediction using deep neural networks. Front. Plant Sci.10, 621. doi: 10.3389/fpls.2019.00621
43
KhanM.JanB.FarmanH.AhmadJ.FarmanH.JanZ. (2019). “Deep learning methods and applications,” in Deep learning: convergence to big data analytics (Singapore: Springer), 31–42.
44
KhanramakiM.Asli-ArdehE. A.KozegarE. (2021). Citrus pests classification using an ensemble of deep learning models. Comput. Electron. Agric.186, 106192. doi: 10.1016/j.compag.2021.106192
45
KhoshbakhtK.HammerK. (2008). How many plant species are cultivated? Genet. Resour. Crop Evol.55, 925–928. doi: 10.1007/s10722-008-9368-0
46
KillamsettyK.SivasubramanianD.RamakrishnanG.IyerR. (2021). “Glister: Generalization based data subset selection for efficient and robust learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, (Menlo Park, Calif: AAAI Press), 35, 8110–8118.
47
KimJ. (2019). Overview of disciplinary data sharing practices and promotion of open data in science. Sci. Ed6, 3–9. doi: 10.6087/kcse.149
48
KimH. K.MinS.SongM.JungS.ChoiJ. W.KimY.et al. (2018). Deep learning improves prediction of crispr–cpf1 guide rna activity. Nat. Biotechnol.36, 239–241. doi: 10.1038/nbt.4061
49
LaiX.StiglianiA.VachonG.CarlesC.SmaczniakC.ZubietaC.et al. (2019). Building transcription factor binding site models to understand gene regulation in plants. Mol. Plant12, 743–763. doi: 10.1016/j.molp.2018.10.010
50
LarsenA. B. L.SønderbyS. K.LarochelleH.WintherO. (2016). “Autoencoding beyond pixels using a learned similarity metric,” in International conference on machine learning (PMLR). (Cambridge: JMLR), 1558–1566.
51
LeCunY.BengioY.HintonG. (2015). Deep learning. nature521, 436–444. doi: 10.1038/nature14539
52
LiK.CarrollM.VafabakhshR.WangX. A.WangJ.-P. (2022a). Dnacycp: a deep learning tool for dna cyclizability prediction. Nucleic Acids Res.50, 3142–3154. doi: 10.1093/nar/gkac162
53
LiL.HassanM. A.YangS.JingF.YangM.RasheedA.et al. (2022b). Development of imagebased wheat spike counter through a faster r-cnn algorithm and application for genetic studies. Crop J.10, 1303–1311. doi: 10.1016/j.cj.2022.07.007
54
LiT.YangX.YuY.SiX.ZhaiX.ZhangH.et al. (2018). Domestication of wild tomato is accelerated by genome editing. Nat. Biotechnol.36, 1160–1163. doi: 10.1038/nbt.4273
55
LiuY.WangD. (2017). “Application of deep learning in genomic selection,” in 2017 ieee international conference on bioinformatics and biomedicine (bibm). (Piscataway: IEEE), 2280.
56
LiuJ.WangX. (2021). Plant diseases and pests detection based on deep learning: a review. Plant Methods17, 1–18. doi: 10.1186/s13007-021-00722-9
57
LongwellS.ShimkoT. (2022). Res2vec: Amino acid vector embeddings from 3d-protein structure. THRESHOLD30, 22–344.
58
MiikkulainenR.LiangJ.MeyersonE.RawalA.FinkD.FranconO.et al. (2019). Evolving deep neural networks. Artif. Intell. age Neural Networks Brain computing, 293–312. doi: 10.1016/B978-0-12-815480-9.00015-3
59
MinaeeS.BoykovY.PorikliF.PlazaA.KehtarnavazN.TerzopoulosD. (2021). Image segmentation using deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell.44, 3523–3542. doi: 10.1109/TPAMI.2021.3059968
60
MirabelloC.WallnerB. (2018). rawmsa: Proper deep learning makes protein sequence profiles and feature extraction obsolete. biorxiv, 394437. doi: 10.1101/394437
61
MooseS. P.MummR. H. (2008). Molecular plant breeding as the foundation for 21st century crop improvement. Plant Physiol.147, 969–977. doi: 10.1104/pp.108.118232
62
NabwireS.WakholiC.FaqeerzadaM. A.AriefM. A. A.KimM. S.BaekI.et al. (2022). Estimation of cold stress, plant age, and number of leaves in watermelon plants using image analysis. Front. Plant Sci.13. doi: 10.3389/fpls.2022.847225
63
NiP.HuangN.ZhangZ.WangD.-P.LiangF.MiaoY.et al. (2019). Deepsignal: detecting dna methylation state from nanopore sequencing reads using deep-learning. Bioinformatics35, 4586–4595. doi: 10.1093/bioinformatics/btz276
64
NirmalaV.GomathyB. (2019). Diagnosis of leaf disease in cucurbita gourd family using machine learning algorithms. Int. J. Recent Technol. Eng.8, 6800–6804. doi: 10.35940/ijrte.C5232.098319
65
NiuZ.ZhongG.YuH. (2021). A review on the attention mechanism of deep learning. Neurocomputing452, 48–62. doi: 10.1016/j.neucom.2021.03.091
66
PanY. (2015). Analysis of concepts and categories of plant phenome and phenomics. Acta Agronom. Sin.41, 175–186. doi: 10.3724/SP.J.1006.2015.00175
67
PanX.ShenH.-B. (2017). Rna-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinf.18, 1–14. doi: 10.1186/s12859-017-1561-8
68
PanS. J.YangQ. (2009). A survey on transfer learning. IEEE Trans. knowl. Data Eng.22, 1345–1359. doi: 10.1109/TKDE.2009.191
69
ParmleyK. A.HigginsR. H.GanapathysubramanianB.SarkarS.SinghA. K. (2019). Machine learning approach for prescriptive plant breeding. Sci. Rep.9, 17132. doi: 10.1038/s41598-019-53451-4
70
PraveenB.SharmaP. (2019). A review of literature on climate change and its impacts on agriculture productivity. J. Public Affairs19, e1960. doi: 10.1002/pa.1960
71
QinQ.FengJ. (2017). Imputation for transcription factor binding predictions based on deep learning. PloS Comput. Biol.13, e1005403. doi: 10.1371/journal.pcbi.1005403
72
RachmatiaH.KusumaW.HasibuanL. (2017). Prediction of maize phenotype based on whole-genome single nucleotide polymorphisms using deep belief networks. J. Phys.: Conf. Ser. (IOP Publishing)835, 012003. doi: 10.1088/1742-6596/835/1/012003
73
RamcharanA.BaranowskiK.McCloskeyP.AhmedB.LeggJ.HughesD. P. (2017). Deep learning for image-based cassava disease detection. Front. Plant Sci.8, 1852. doi: 10.3389/fpls.2017.01852
74
SandhuK. S.AounM.MorrisC. F.CarterA. H. (2021). Genomic selection for end-use quality and processing traits in soft white winter wheat breeding program with machine and deep learning models. Biology10, 689. doi: 10.3390/biology10070689
75
SChadtE. E.LiC.EllisB.WongW. H. (2001). Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J. Cell. Biochem.84, 120–125. doi: 10.1002/jcb.10073
76
ScharrH.MinerviniM.FrenchA. P.KlukasC.KramerD. M.LiuX.et al. (2016). Leaf segmentation in plant phenotyping: a collation study. Mach. Vision Appl.27, 585–606. doi: 10.1007/s00138-015-0737-3
77
SchreiberJ.LibbrechtM.BilmesJ.NobleW. S. (2017). Nucleotide sequence and dnasei sensitivity are predictive of 3d chromatin architecture. BioRxiv, 103614. doi: 10.1101/103614
78
ShaikhT. A.RasoolT.LoneF. R. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Comput. Electron. Agric.198, 107119. doi: 10.1016/j.compag.2022.107119
79
ShaoB.HouY.HuangN.WangW.LuX.JingY. (2022). “Deep learning based coffee beans quality screening,” in 2022 IEEE International Conference on e-Business Engineering (ICEBE). (Piscataway: IEEE), 271–275.
80
ShenY.ZhouG.LiangC.TianZ. (2022). Omics-based interdisciplinarity is accelerating plant breeding. Curr. Opin. Plant Biol.66, 102167. doi: 10.1016/j.pbi.2021.102167
81
SheteS.SrinivasanS.GonsalvesT. A. (2020). Tasselgan: An application of the generative adversarial model for creating field-based maize tassel data. Plant Phenom.2020, 1–15. doi: 10.34133/2020/8309605
82
ShortenC.KhoshgoftaarT. M. (2019). A survey on image data augmentation for deep learning. J. big Data6, 1–48. doi: 10.1186/s40537-019-0197-0
83
ShrikumarA.GreensideP.KundajeA. (2017). “Learning important features through propagating activation differences,” in International conference on machine learning. (Cambridge: PMLR), 3145–3153.
84
SimonyanK.VedaldiA.ZissermanA. (2013). Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv. 1312.6034, 1–7. doi: 10.48550/arXiv.1312.6034
85
SnellJ.SwerskyK.ZemelR. (2017). Prototypical networks for few-shot learning. Adv. Neural Inf. Process. Syst.30, 4080–4090. doi: 10.48550/arXiv.1703.05175
86
SonyS.LaventureS.SadhuA. (2019). A literature review of next-generation smart sensing technology in structural health monitoring. Struct. Control Health Monit.26, e2321. doi: 10.1002/stc.2321
87
SunX.NasrabadiN. M.TranT. D. (2018). “Supervised deep sparse coding networks,” in 2018 25th IEEE International Conference on Image Processing (ICIP). (Piscataway: IEEE), 346–350.
88
SunD.ZhuY.XuH.HeY.CenH. (2019). Time-series chlorophyll fluorescence imaging reveals dynamic photosynthetic fingerprints of sos mutants to drought stress. Sensors19, 2649. doi: 10.3390/s19122649
89
Taghavi NaminS.EsmaeilzadehM.NajafiM.BrownT. B.BorevitzJ. O. (2018). Deep phenotyping: deep learning for temporal phenotype/genotype classification. Plant Methods14, 1–14. doi: 10.1186/s13007-018-0333-4
90
TalbotB.ChenT.-W.ZimmermanS.JoostS.EckertA. J.CrowT. M.et al. (2017). Combining genotype, phenotype, and environment to infer potential candidate genes. J. Heredity108, 207–216. doi: 10.1093/jhered/esw077
91
TongY.-S.Tou-HongL.YenK.-S. (2022). Deep learning for image-based plant growth monitoring: A review. Int. J. Eng. Technol. Innovation12, 225. doi: 10.46604/ijeti.2022.8865
92
TranN. H.ZhangX.XinL.ShanB.LiM. (2017). De novo peptide sequencing by deep learning. Proc. Natl. Acad. Sci.114, 8247–8252. doi: 10.1073/pnas.1705691114
93
TunyasuvunakoolK.AdlerJ.WuZ.GreenT.ZielinskiM.ŽídekA.et al. (2021). Highly accurate protein structure prediction for the human proteome. Nature596, 590–596. doi: 10.1038/s41586-021-03828-1
94
UzalL. C.GrinblatG. L.NamíasR.LareseM. G.BianchiJ. S.MorandiE. N.et al. (2018). Seedper-pod estimation for plant breeding using deep learning. Comput. Electron. Agric.150, 196–204. doi: 10.1016/j.compag.2018.04.024
95
WallaceJ. G.Rodgers-MelnickE.BucklerE. S. (2018). On the road to breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics. Annu. Rev. Genet.52, 421–444. doi: 10.1146/annurev-genet-120116-024846
96
WangK.AbidM. A.RasheedA.CrossaJ.HearneS.LiH. (2023). Dnngp, a deep neural network-based method for genomic prediction using multi-omics data in plants. Mol. Plant16, 279–293. doi: 10.1016/j.molp.2022.11.004
97
WangH.CimenE.SinghN.BucklerE. (2020). Deep learning for plant genomics and crop improvement. Curr. Opin. Plant Biol.54, 34–41. doi: 10.1016/j.pbi.2019.12.010
98
WangM.TaiC.WeinanE.WeiL. (2018). Define: deep convolutional neural networks accurately quantify intensities of transcription factor-dna binding and facilitate evaluation of functional noncoding variants. Nucleic Acids Res.46, e69. doi: 10.1093/nar/gky215
99
XieR.WenJ.QuitadamoA.ChengJ.ShiX. (2017). A deep auto-encoder model for gene expression prediction. BMC Genomics18, 39–49. doi: 10.1186/s12864-017-4226-0
100
XuW.GaoY.WangY.GuanJ. (2021). Protein–protein interaction prediction based on ordinal regression and recurrent convolutional neural networks. BMC Bioinf.22, 1–20. doi: 10.1186/s12859-021-04369-0
101
YanJ.WangX. (2022). Machine learning bridges omics sciences and plant breeding. Trends Plant Sci. 28, 199–211. doi: 10.1016/j.tplants.2022.08.018
102
YangH.ChiH.ZengW.-F.ZhouW.-J.HeS.-M. (2019). pnovo 3: precise de novo peptide sequencing using a learning-to-rank framework. Bioinformatics35, i183–i190. doi: 10.1093/bioinformatics/btz366
103
YangZ.GaoS.XiaoF.LiG.DingY.GuoQ.et al. (2020). Leaf to panicle ratio (lpr): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning. Plant Methods16, 1–15. doi: 10.1186/s13007-020-00660-y
104
YangB.LiuF.RenC.OuyangZ.XieZ.BoX.et al. (2017). Biren: predicting enhancers with a deep-learning-based model using the dna sequence alone. Bioinformatics33, 1930–1936. doi: 10.1093/bioinformatics/btx105
105
YangY.SaandM. A.HuangL.AbdelaalW. B.ZhangJ.WuY.et al. (2021). Applications of multi-omics technologies for crop improvement. Front. Plant Sci.12, 563953. doi: 10.3389/fpls.2021.563953
106
YinJ.HuoL.GuoL.HuJ. (2008). “Short-term load forecasting based on improved gene expression programming,” in 2008 7th World Congress on Intelligent Control and Automation. (Piscataway: IEEE), 5647–5650.
107
ZampieriG.VijayakumarS.YaneskeE.AngioneC. (2019). Machine and deep learning meet genome-scale metabolic modeling. PloS Comput. Biol.15, e1007084. doi: 10.1371/journal.pcbi.1007084
108
ZengH.GiffordD. K. (2017). Predicting the impact of non-coding variants on dna methylation. Nucleic Acids Res.45, e99. doi: 10.1093/nar/gkx177
109
ZhangY.AnL.XuJ.ZhangB.ZhengW. J.HuM.et al. (2018). Enhancing hi-c data resolution with deep convolutional neural network hicplus. Nat. Commun.9, 750. doi: 10.1038/s41467-018-03113-2
110
ZhangD.KabukaM. (2019). Multimodal deep representation learning for protein interaction identification and protein family classification. BMC Bioinf.20, 1–14. doi: 10.1186/s12859-019-3084-y
111
ZhangZ.ParkC. Y.TheesfeldC. L.TroyanskayaO. G. (2021b). An automated framework for efficiently designing deep convolutional neural networks in genomics. Nat. Mach. Intell.3, 392–400. doi: 10.1038/s42256-021-00316-z
112
ZhangF.WenY.GuoX. (2014). Crispr/cas9 for genome editing: progress, implications and challenges. Hum. Mol. Genet.23, R40–R46. doi: 10.1093/hmg/ddu125
113
ZhangJ. X.YordanovB.GauntA.WangM. X.DaiP.ChenY.-J.et al. (2021a). A deep learning model for predicting next-generation sequencing depth from dna sequence. Nat. Commun.12, 4387. doi: 10.1038/s41467-021-24497-8
114
ZhouX. (2018). Understanding the convolutional neural networks with gradient descent and backpropagation. J. Phys.: Conf. Ser. (IOP Publishing)1004, 012028. doi: 10.1088/1742-6596/1004/1/012028
115
ZhouJ.TheesfeldC. L.YaoK.ChenK. M.WongA. K.TroyanskayaO. G. (2018). Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat. Genet.50, 1171–1179. doi: 10.1038/s41588-018-0160-6
116
ZhouJ.TroyanskayaO. G. (2015). Predicting effects of noncoding variants with deep learning– based sequence model. Nat. Methods12, 931–934. doi: 10.1038/nmeth.3547
117
ZitnikM.AgrawalM.LeskovecJ. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics34, i457–i466. doi: 10.1093/bioinformatics/bty294
118
ZitnikM.LeskovecJ. (2017). Predicting multicellular function through multi-layer tissue networks. Bioinformatics33, i190–i198. doi: 10.1093/bioinformatics/btx252
Summary
Keywords
crop breeding, deep learning, smart breeding, challenge, prospect
Citation
Wang X, Zeng H, Lin L, Huang Y, Lin H and Que Y (2023) Deep learning-empowered crop breeding: intelligent, efficient and promising. Front. Plant Sci. 14:1260089. doi: 10.3389/fpls.2023.1260089
Received
18 July 2023
Accepted
13 September 2023
Published
03 October 2023
Volume
14 - 2023
Edited by
Daojun Yuan, Huazhong Agricultural University, China
Reviewed by
Xiujun Zhang, Chinese Academy of Sciences (CAS), China; Dong-Liang Huang, Guangxi Academy of Agricultural Sciences, China
Updates
Copyright
© 2023 Wang, Zeng, Lin, Huang, Lin and Que.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Youxiong Que, queyouxiong@126.com
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