Machine Learning-Assisted Plant Genome Editing

The genome editing technique is a powerful tool for precision agriculture as it permits the modification of DNA sequences with the aim of crop improvemen—enhancing crop yield, improving stress tolerance, increasing resistance to pests and pathogens, and boosting nutritional qualities among others. Nevertheless, genome editing in plants is a critical and taxing operation as it requires precision in the way the enzymes work, how the editing process takes place, and the nature of the targets. With the advent of deep machine learning and big data, it is now possible to predict editing results and determine the parameters of genome editing. Machine learning has made genome editing easier and has led to the fine-tuning of the process. This chapter will primarily focus on the standardization of genome editing with the help of machine learning with a special focus on editing efficiency, editing specificity, and also prediction of genome editing by machine learning. The chapter will try to bridge the gap between plant breeding and genome editing through machine learning. Additionally, the chapter will also highlight the use of AI in genome editing. Further, it will try to analyze the opportunities and challenges of using machine learning in genome editing techniques. Combining genome editing with machine learning will definitely accelerate the progress of crop improvement.