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Artificial Intelligence and Machine Learning in Rice Research

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Applications of Bioinformatics in Rice Research

Abstract

Food security is a significant challenge for emerging and underdeveloped countries all over the world. The world’s population is growing tremendously; therefore, more food will be required to fulfill their needs. As a result, farmers and breeders put more pressure on agricultural land to produce more food grains. Rice is a primary food crop for 1.3 billion people globally, with Asia contributing for 90% of rice production and consumption. In undeveloped nations, the majority of farmers rely on traditional farming methods, which are insufficient to meet rising food grain demand. Their poor farming methods must be wreaking havoc on the land by utilizing more harmful pesticides and chemical fertilizers. As a result, a destructive impact on soil microfauna and flora affects soil nutritional quality, agricultural practices, and the soil becomes barren with the loss of fertility. In the future, agriculture automation is a big concern for all countries to feed and provide food security to their population. This book chapter focuses on evaluating and exploring a variety of published solutions, methods, and perspectives on “artificial intelligence (AI),” “machinelearning”, “big data applications”, and “high-performance computing,” which have opened up new avenues for data-intensive research (ML) in multidisciplinary agro-technology. Thus, there is an immediate need for various automation methods to decode rice and other crop cultivation problems, farm management, agricultural practices, irrigation, fertilization, weeding, harvesting, marketing, food distributions, toxic pesticides, regulated drainage, emissions regulation, and environmental impact. The automation of farming activities will help to improve soil benefits and strengthening soil fertility. Therefore, the advancing practical application and solutions pave the way for a modern data-centric age of agriculture exploration.

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Abbreviations

AI:

Artificial intelligence

KNN:

k-nearest neighbor

LARS:

Least-angle regression

LASSO:

Least absolute shrinkage and selection operator

LOESS:

Locally estimated scatterplot smoothing

LVQ:

Learning vector quantization

LWL:

Locally weighted learning

MARS:

Multivariate adaptive regression splines

ML:

Machine learning

MLR:

Multiple linear regression

OLSR:

Ordinary least squares regression

PCA:

Principal component analysis

PCR:

Principal component regression

PLS:

Partial least squares regression

SOM:

Self-organizing map

SVM:

Support vector machines

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Authors and Affiliations

  1. Crop Improvement Division, ICAR-National Rice Research Institute, Cuttack, Odisha, India

    Ravindra Donde, Gayatri Gouda, S. Sabarinathan, Manoj Kumar Gupta, Goutam Kumar Dash, Sushanta Kumar Dash, Priyadarsini Sanghamitra, C. Parameswaran & Lambodar Behera

  2. Department of Seed Science and Technology, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India

    S. Sabarinathan

  3. Department of Biotechnology & Bioinformatics, Yogi Vemana University, Kadapa, Andhra Pradesh, India

    N. Rajesh & Ramakrishna Vadde

  4. Department of Botany, Ravenshaw University, Cuttack, Odisha, India

    Khirod Kumar Sahoo

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Donde, R. et al. (2021). Artificial Intelligence and Machine Learning in Rice Research. In: Gupta, M.K., Behera, L. (eds) Applications of Bioinformatics in Rice Research. Springer, Singapore. https://doi.org/10.1007/978-981-16-3997-5_12

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