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

Author

Listed:
  • Meenakshi Sharma

    (Department of Chemistry, Kurukshetra University Kurukshetra, Kurukshetra 136119, Haryana, India)

  • Prashant Kaushik

    (Kikugawa Research Station, Yokohama Ueki, 2265, Kamo, Kikugawa City, Shizuoka 439-0031, Japan
    Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Aakash Chawade

    (Department of Plant Breeding, Swedish University of Agricultural Sciences, SE-23053 Alnarp, Sweden)

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.

Suggested Citation

  • Meenakshi Sharma & Prashant Kaushik & Aakash Chawade, 2021. "Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research," Sustainability, MDPI, vol. 13(15), pages 1-14, August.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8600-:d:606790
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    References listed on IDEAS

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    1. Robert Finger & Stéphanie Schmid, 2008. "Modeling agricultural production risk and the adaptation to climate change," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 68(1), pages 25-41, May.
    2. Gurdeep Singh Malhi & Manpreet Kaur & Prashant Kaushik, 2021. "Impact of Climate Change on Agriculture and Its Mitigation Strategies: A Review," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
    3. Mohsen Niazian & Gniewko Niedbała, 2020. "Machine Learning for Plant Breeding and Biotechnology," Agriculture, MDPI, vol. 10(10), pages 1-23, September.
    4. Kocev, Dragi & Džeroski, Sašo & White, Matt D. & Newell, Graeme R. & Griffioen, Peter, 2009. "Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition," Ecological Modelling, Elsevier, vol. 220(8), pages 1159-1168.
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    Cited by:

    1. Ewa Ropelewska & Xiang Cai & Zhan Zhang & Kadir Sabanci & Muhammet Fatih Aslan, 2022. "Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum ( Prunus domestica L.) Kernels," Agriculture, MDPI, vol. 12(2), pages 1-12, February.

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