An Integrative Approach Towards Recommending Farming Solutions for Sustainable Agriculture

Veena Ghuriani; Jyotsna Talreja Wassan; Pragya Deolal; Vidushi Sharma; Dimpy Dalal; Aditi Goyal

doi:10.18006/2023.11(2).306.315

Authors

Veena Ghuriani Department of Computer Science, Maitreyi College, University of Delhi, Delhi, India https://orcid.org/0009-0004-6595-171X
Jyotsna Talreja Wassan Department of Computer Science, Maitreyi College, University of Delhi, Delhi, India https://orcid.org/0009-0002-3054-0764
Pragya Deolal Department of Computer Science, Maitreyi College, University of Delhi, Delhi, India https://orcid.org/0009-0003-0365-3023
Vidushi Sharma Department of Computer Science, Maitreyi College, University of Delhi, Delhi, India https://orcid.org/0000-0002-5834-1401
Dimpy Dalal Department of Computer Science, Maitreyi College, University of Delhi, Delhi, India https://orcid.org/0009-0005-3185-3975
Aditi Goyal Department of Computer Science, Maitreyi College, University of Delhi, Delhi, India https://orcid.org/0009-0007-5912-3450

DOI:

https://doi.org/10.18006/2023.11(2).306.315

Keywords:

Sustainable agriculture, Precision Farming (PF), Zero Budget Natural Farming (ZBNF), Machine Learning (ML), Classification

Abstract

Sustainable Agriculture is rapidly emerging as an important discipline to meet societal needs for food and other resources by adopting paradigms of conserving natural resources while maximizing productivity benefits. This paper proposes an integrative methodological approach for critically analyzing Precision Farming (PF) paradigms and Zero Budget Natural Farming (ZBNF), providing sustainable farming solutions and achieving productivity and profitability. This paper analyses the productivity of crops in PF using various machine learning (ML) algorithms based on different soil and climatic factors to identify sustainable agricultural practices for maximizing crop production and generating recommendations for the farmers. When implemented on the collected dataset from various Indian states, the Random Forest (RF) model produced the best results with an AUC-ROC of 95.7%. The Juxtaposition of ZBNF and non-ZBNF is evinced. ZBNF is statistically (p<0.05) observed to be a cost-efficient and more profitable alternative. The impact of ZBNF on soil microbial diversity and micro-nutrients is also discussed.

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