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Bioinformatics in Agriculture and Ecology Using Few-Shots Learning From Field to Conservation

Bioinformatics in Agriculture and Ecology Using Few-Shots Learning From Field to Conservation

Jayashri Prashant Shinde, Smitha Nayak, Deepika Amol Ajalkar, Yogesh Kumar Sharma
Copyright: © 2024 |Pages: 12
ISBN13: 9798369318225|ISBN13 Softcover: 9798369345047|EISBN13: 9798369318232
DOI: 10.4018/979-8-3693-1822-5.ch002
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MLA

Shinde, Jayashri Prashant, et al. "Bioinformatics in Agriculture and Ecology Using Few-Shots Learning From Field to Conservation." Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods, edited by Umesh Kumar Lilhore, et al., IGI Global, 2024, pp. 27-38. https://doi.org/10.4018/979-8-3693-1822-5.ch002

APA

Shinde, J. P., Nayak, S., Ajalkar, D. A., & Sharma, Y. K. (2024). Bioinformatics in Agriculture and Ecology Using Few-Shots Learning From Field to Conservation. In U. Lilhore, A. Kumar, S. Simaiya, N. Vyas, & V. Dutt (Eds.), Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods (pp. 27-38). IGI Global. https://doi.org/10.4018/979-8-3693-1822-5.ch002

Chicago

Shinde, Jayashri Prashant, et al. "Bioinformatics in Agriculture and Ecology Using Few-Shots Learning From Field to Conservation." In Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods, edited by Umesh Kumar Lilhore, et al., 27-38. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1822-5.ch002

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Abstract

The integration of bioinformatics with contemporary machine-learning algorithms is transforming sustainable practices and conservation activities in biology and agriculture. Plant disease identification is an area where few-shot learning (FSL) excels because of data scarcity. This study applies FSL to computational biology to tackle agricultural and environmental concerns. Bioinformatics has a significant influence on sustainable farming and research, according to the report. The chapter introduces few-shot learning, and shows how it may address the lack of labelled data in several disciplines. Case studies, including explanations, demonstrate the manner in which the FSL method is widely used in ecological surveillance, environmental programs, and crop supervisors. The essay discusses ethical issues around machine learning in ecological systems and agriculture, emphasizing open and responsible data methods.

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