Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research

Abstract

Along with essential nutrients and trace elements, vegetables provide raw materials for the food processing industry. Despite this, plant diseases and unfavorable weather patterns continue to threaten the delicate balance between vegetable production and consumption. It is critical to utilize machine learning (ML) in this setting because it provides context for decision-making related to breeding goals. Cutting-edge technologies for crop genome sequencing and phenotyping, combined with advances in computer science, are currently fueling a revolution in vegetable science and technology. Additionally, various ML techniques such as prediction, classification, and clustering are frequently used to forecast vegetable crop production in the field. In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly; whereas, in plant disease detection and management, the ML approaches can improve decision-support systems that assist in converting massive amounts of data into valuable recommendations. On similar lines, in vegetable breeding, ML approaches are helpful in predicting treatment results, such as what will happen if a gene is silenced. Furthermore, ML approaches can be a saviour to insufficient coverage and noisy data generated using various omics platforms. This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing.

Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum (Prunus domestica L.) Kernels

“…Machine learning algorithms can improve the decision-support system. Due to the many possibilities of application, machine learning can be used even more extensively in the future [32].…”

Vegetable Breeding: Status and Strategies

“…Cutting-edge technologies for crop genome sequencing and phenotyping combined with advances in computer science are currently fuelling a revolution in vegetable science (Sharma et al, 2021). AI also called machine intelligence is a domain in computer science that instructs machines on how to replicate human physical actions and react like humans.…”

“…Similarly, ML has been widely used to decipher the relationship between DNA sequences and observed phenotypes in both conventional and in vitro plant breeding research. ML is currently in use for the assessment of seed quality, disease detection and control, prediction of climatic variations, crop monitoring and yield prediction (Sharma et al, 2021).…”

Systematic Review - State of the Art: strategies for vigor evaluation and production of high-vigor soybean seeds

“…New computer vision solutions, combined with artificial intelligence algorithms, can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process (Medeiros et al, 2020b). Similarly, the integration of seed phenotype image datasets with data from genomic and environmental domains can be used to gain insights for intelligent reproduction (Sharma et al, 2021).…”