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Automation in Agriculture: A Systematic Survey of Research Activities in Agriculture Decision Support Systems Using Machine Learning

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Futuristic Trends in Networks and Computing Technologies

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 936))

Abstract

In this age of automation, Machine learning (ML) plays the main role in agriculture sector to suggest suitable advice, crop advice, which includes decisions of growing crops, and advice related to growing season for precision farming. This systematic literature review performs a review of 103 documents of different ML approaches to analyze the performance of algorithms and used features in the work of prediction of crop yield and decision support systems to solve agriculture problems. These 103 documents are retrieved from different electronic databases, for analysis. The paperwork presents methods, accuracy measures, and used agriculture parameters, to understand the existing work done by authors. According to analysis, most of the authors used N, P, and K values and type of soil, and most of the authors used classification techniques such as Support Vector Machine, Decision Trees, Regression techniques, Random Forest, and Naive Bayes algorithm; the most applied clustering algorithm in the existing work is K-means. As per the additional survey, the Convolution Neural Network (CNN) algorithm is used by most of the authors for image processing in their work. Also, survey shows that very few authors used associative classifiers and association rule mining techniques to solve the agriculture problems.

Madan Lal Saini: This author contributed equally to this work.

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  1. Department of Computer Science and Engineering, Poornima University, Jaipur, 303905, Rajasthan, India

    Sushma Vispute & Madan Lal Saini

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  1. Sushma Vispute
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  1. KIET Group of Institutions, Ghaziabad, India

    Pradeep Kumar Singh

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Sławomir T. Wierzchoń

  3. Department of Computer Engineering, NIT Kurukshetra, Haryana, India

    Jitender Kumar Chhabra

  4. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

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Vispute, S., Saini, M.L. (2022). Automation in Agriculture: A Systematic Survey of Research Activities in Agriculture Decision Support Systems Using Machine Learning. In: Singh, P.K., Wierzchoń, S.T., Chhabra, J.K., Tanwar, S. (eds) Futuristic Trends in Networks and Computing Technologies . Lecture Notes in Electrical Engineering, vol 936. Springer, Singapore. https://doi.org/10.1007/978-981-19-5037-7_56

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