Sugar beet ( Beta vulgaris L.) has emerged as an alternative to sugarcane. It is mainly utilized for sugar extraction and has significant industrial value with great nutritional impact. Different kinds of biotic and abiotic stresses are considered to be major barriers for sugar beet cultivation. As per the current scenario, every year sugar beet production suffers huge yield losses due to various stresses. The conventional breeding technique is a time-consuming lengthy procedure which can be replaced by a genetic transformation technique to bring new transgenic traits within a short period of time. Sugar beet has proven to be excellent sample material for in vitro culture of haploid plants, protoplast culture, somaclonal variation, and single cell culture, among others. Agrobacterium mediated and PEG-mediated transformations are the most effective genomic transformations in the case of sugar beet. Development of new traits in terms of fungus/virus, pest/nematode tolerance, herbicide and salt tolerance are the most frequently expected traits in the current scenario of sugar beet production. Potential transgenic plants are viable alternatives to traditional expression systems for end product (protein) development with more accuracy. So, transgenic production through genome editing/base editing is presently considered to be one of the best tools for sugar beet tolerant traits development. Food safety and environmental impacts are two major concerns of genetic transformation in sugar beet and need to be appropriately screened for public health acceptability.

The world population, and thus the need for food, is increasing every day. This leads to the ultimate question of how to increase food production with limited time and scarce land. Another obstacle to meet the food demand includes the stresses a plant goes through. These may be abiotic or biotic, but the majority are biotic, i.e., plant diseases. The major challenge is to mitigate plant diseases efficiently, more quickly and with less manpower. Recently, artificial intelligence has turned to new frontiers in smart agricultural science. One novel approach in plant science is to detect and diagnose plant disease through deep learning and hyperspectral imaging. This smart technique is very advantageous for monitoring large acres of field where the availability of manpower is a major drawback. Early identification of plant diseases can be achieved through machine learning approaches. Advanced machine learning not only detects diseases but also helps to discover gene regulatory networks and select the genomic sequence to develop resistance in crop species and to mark pathogen effectors. In this review, new advancements in plant science through machine learning approaches have been discussed.