With the global population projected to reach nearly 10 billion by 2050, and climate change altering growing conditions, plant breeders must accelerate their research to develop crops with high yields and resilience to climatic challenges. Traditional statistical methods have been essential in this field, but the complexity and volume of modern data necessitate more advanced tools. Machine learning (ML) is revolutionizing plant breeding by offering precise predictions and effective data management, thereby facilitating the development of superior crop varieties.
Machine learning encompasses various tools such as convolutional neural networks (CNNs), artificial neural networks (ANNs), random forest (RF), support vector machines (SVMs), reproducing kernel Hilbert space (RKHS), and deep neural networks (DNNs). These techniques can manage, categorize, and predict complex interactions between numerous variables involved in plant breeding, enhancing decision-making processes.Plants face various stresses throughout their lifecycle. Traditional statistical methods like harmonic mean (HARM), yield stability index (YSI), and stress tolerance index (STI) are used to assess stress tolerance.
However, integrating phenomics with genomic and metabolomic data via machine learning can predict stress responses more accurately. For instance, combining imaging techniques with ML can simulate plant responses to stress conditions, identifying resistant variants.Genetic diversity is critical for plant breeding programs. Traditional methods like principal component analysis (PCA) and cluster analysis are time-consuming and complex.
Machine learning algorithms, such as CNNs and ANNs, streamline this process by automating feature extraction and object detection, enhancing accuracy and efficiency. Yield improvement is a primary goal of plant breeding, but it is often influenced by environmental factors. Traditional methods like multiple regression and PCA can be insufficient due to their linear nature. Nonlinear ML algorithms, particularly ANNs, offer better yield predictions by analyzing the complex relationships between yield components and environmental factors.
Predicting the heritability of traits in crossbreeding programs requires extensive analysis of gene actions and phenotypic traits. ML algorithms like ANNs can predict parental combinations more accurately, aiding in the selection of superior hybrid varieties.
Yield Stability and Genotype x Environmental Interactions (GEI) causes year-to-year yield variations, complicating genotype selection. Traditional univariate approaches are often inadequate for multivariable analyses. ML techniques, especially ANNs, provide more accurate predictions of yield stability by efficiently handling multiple variables.Biotechnological advancements have introduced in vitro techniques like plant regeneration and gene editing, which require complex data analysis. ML algorithms can integrate these data for better predictions and outcomes in in vitro studies, such as artificial polyploidy induction and gene transformation.The genotype-to-phenotype gap is a significant challenge in modern plant breeding. Advanced sequencing technologies produce large datasets with potential errors.
ML tools like CNNs can improve sequence analysis and variant prediction, enhancing genomic studies’ accuracy.Phenotyping, crucial for linking genotypes with phenotypes, traditionally relies on manual measurements, limiting its scope. ML techniques, particularly deep learning, can automate image-based phenotyping, improving accuracy and throughput in tasks like disease detection and growth analysis.ML-assisted breeding optimizes resource use by reducing the number of plants needed for trait development, saving time, money, and natural resources. Precise data collection enables better predictions tailored to specific environmental conditions, enhancing sustainability.
Machine learning is transforming plant breeding by handling complex, large datasets more effectively than traditional methods. Its application spans various areas, from stress assessment to yield prediction and resource management. By leveraging ML, plant breeders can develop resilient, high-yield crops, crucial for meeting the food demands of the growing global population amid climatic changes. As the world strives to ensure food security for 9.1 billion people by 2050, ML will be instrumental in achieving this goal.
(Author is a Scientist- MRCFC- Khudwani, SKUAST-Kashmir. Feedback; [email protected])
