Future of Indian Agriculture Using AI and Machine Learning Tools and Techniques

Kumar, Manoj; Maurya, Pratibha; Verma, Rinki

doi:10.1002/9781119884392.ch19

Cited by 2 publications

(2 citation statements)

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“…These techniques can be employed in crop yield prediction, soil health analysis, pest and disease detection, and precision farming practices. Machine learning algorithms can analyze vast datasets, including historical agricultural data, weather patterns, and soil conditions, to develop predictive models for crop yields [6][7][8][9][10]. By understanding the intricate relationships between different variables, these models can provide valuable insights for farmers to enhance productivity and resource efficiency.…”

Section: Machine Learning In Agricultural Growthmentioning

confidence: 99%

“…Despite the time investment required to obtain representative datasets, it is crucial for the effectiveness and reliability of ML applications in agriculture. Collaborative efforts with farmers, agricultural institutions, and research organizations can enhance data pooling and contribute to the success of ML applications [5][6][7][8]. This review aims to investigate the potential of ML techniques in driving agricultural growth within the unique context of Assam's economy.…”

Section: Introductionmentioning

confidence: 99%

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Recent Trends of Machine Learning Techniques on The Growth of Agricultural Sector of Assam

Borthakur,

Sahoo,

Mahanta

et al. 2023

ijmst

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The agricultural sector in Assam, India, holds immense potential for economic growth and rural development. However, harnessing this potential requires tackling challenges like low productivity, resource scarcity, and climate change. Machine learning (ML) emerges as a promising tool to address these hurdles and transform Assam’s agricultural sector. This review investigates the potential of machine learning (ML) techniques in driving agricultural growth within the specific context of the Assam’s economy. Utilizing comprehensive search within Scopus and Web of Science databases from 2015 to 2023, and following the PRISMA guidelines, we analyzed 37 relevant articles. Our examination focuses on the multifaceted applications of ML across various agricultural domains in Assam, encompassing crop yield prediction, soil health analysis and economic growth. The review highlights successful ML- driven interventions in Assam’s agricultural sector, showcasing their ability to improve resource efficiency, optimize crop management, and enhance market access. This review provides valuable insights for policymakers, researchers, and farmers seeking to leverage the power of ML for a more sustainable and prosperous Assam’s agricultural landscape.

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Section: Machine Learning In Agricultural Growthmentioning

confidence: 99%