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

Sharma, Meenakshi and Kaushik, Prashant and Chawade, Aakash (2021). Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research. Sustainability. 13 , 8600
[Article Review/Survey]

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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.

Authors/Creators:Sharma, Meenakshi and Kaushik, Prashant and Chawade, Aakash
Title:Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research
Series Name/Journal:Sustainability
Year of publishing :2021
Volume:13
Article number:8600
Number of Pages:14
Publisher:MDPI
ISSN:2071-1050
Language:English
Publication Type:Article Review/Survey
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Agricultural Science
(A) Swedish standard research categories 2011 > 1 Natural sciences > 102 Computer and Information Science > 10203 Bioinformatics (Computational Biology) (applications to be 10610)
Keywords:machine learning, vegetables, models, predictions, breeding, biotechnology, genomics
URN:NBN:urn:nbn:se:slu:epsilon-p-113257
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-113257
Additional ID:
Type of IDID
DOI10.3390/su13158600
Web of Science (WoS)000682215600001
ID Code:25166
Faculty:LTV - Fakulteten för landskapsarkitektur, trädgårds- och växtproduktionsvetenskap
Department:(LTJ, LTV) > Department of Plant Breeding (from 130101)
Deposited By: SLUpub Connector
Deposited On:02 Sep 2021 14:27
Metadata Last Modified:02 Sep 2021 14:31

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