Towards a Greener Tomorrow: The Role of Data Science in Shaping Sustainable Farming Practices

Main Article Content

Kasula Kedhari Priya
Shaik Abdul Khadeer
S. Kiran
A. Ashok Kumar
Chinnem Rama Mohan

Abstract

- Sustainable farming practices are essential for addressing global food security challenges while minimizing environmental impact. However, traditional agricultural methods face numerous obstacles, including impacts of climate change, resource scarcity, and population growth, creating a necessity to incorporate innovative approaches to ensure long-term sustainability. This review aims to comprehensively explore the integration of data science within the agricultural domain to enhance sustainability. By examining the role of data science in addressing agricultural challenges, the review aims to illuminate how data-driven approaches can revolutionize farming practices. The review adopts a systematic approach to analyze the existing literature on data science applications in agriculture. Criteria for selecting relevant studies include their focus on data science techniques and their applicability to agricultural contexts. Data science techniques have made significant contributions to sustainable agricultural practices. Key findings reveal that the collaborative efforts among Data Scientists, Agronomists, IT and Software Engineers, and Decision-Makers are crucial in addressing challenges such as data privacy, security, and scalability. Various algorithmic approaches, such as predictive analytics and machine learning models, are showcased for crop and soil management, irrigation systems, and supply chain optimization. Data science has vast practical implications for farming. It can improve efficiency, productivity, and environmental impact, paving the way for a more sustainable and resilient agricultural future.

Article Details

How to Cite
[1]
Kasula Kedhari Priya, Shaik Abdul Khadeer, S. Kiran, A. Ashok Kumar, and Chinnem Rama Mohan, “Towards a Greener Tomorrow: The Role of Data Science in Shaping Sustainable Farming Practices”, Int. J. Comput. Eng. Res. Trends, vol. 11, no. 4, pp. 12–19, Apr. 2024.
Section
Reviews

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