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

• Published in: Sustainability 2021-08, Vol.13 (15), p.8600
• Main Authors: Sharma, Meenakshi, Kaushik, Prashant, Chawade, Aakash
• Format: Article
• Language: English
• Subjects: Agricultural production; Agricultural Science; Algorithms; Bioinformatics (Computational Biology); Bioinformatik (beräkningsbiologi); Biotechnology; Breeding; Climate change; Clustering; Crop diseases; Crop production; Crops; Decision making; Disease detection; Economic forecasting; Essential nutrients; Food industry; Food processing; Food processing industry; Genomes; Germination; Jordbruksvetenskap; Learning algorithms; Machine learning; Methods; Neural networks; Nutrients; Phenotyping; Plant breeding; Plant diseases; Processing industry; Quality assessment; Raw materials; Regression analysis; Science; Seeds; Support vector machines; Sustainability; Trace elements; Variables; Vegetables; Weather patterns
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su13158600

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.