Digital Technologies in Crop Genotype Designing Methods: Scope, Limitations and Future Perspectives

Abstract

The modern world agricultural sector has come under severe attack from several factors. These factors range from biotic to abiotic factors and they present threats to the environment and the world economies at large. If agricultural production is made more sustainable, it can be able to combat the current food shortages. Looking into the present scenario, there is a great need to improve the traditional breeding designing methods to develop genotypes of different crops that would be able to withstand the current adverse effects brought about by persistent climate change. Central to the basis and key factor of improving the designing methods in crop production are different digital technologies such as Artificial Intelligence (AI), Deep Learning (DL), Machine Learning (ML), Geographical Information System (GIS), Precision Agriculture (PA), and Remote Sensing (RS).  The digitalization of traditional breeding strategies has its weaknesses in terms of genetic gains it could offer in improving crop production. However, improving digital technologies would result in improved designing methods of crop production that would consequently result in increasing agricultural production and productivity. Therefore, the current review highlights the gains that have been made especially by AI and ML in designing methods of crop production. In addition, the review also highlights the limitations of these digital tools and their potential in crop designing methods for future crop genetic gains and production as well.