Forecasting Agricultural Business Development based on Seasonal Demand using Machine Learning Techniques

11 pages•Published: August 6, 2024

Mahendhiran P D, Aakash V, Arees Muhmmed S, Rithanya S and Varun S

Abstract

Agriculture, humanity's foundational activity, encompasses a rich tapestry of practices essential for sustaining life on our planet. From the cultivation of crops and the rearing of livestock to the management of forests and fisheries, agriculture stands as the bedrock of food production, economic development, and environmental stewardship. Across cultures and civilizations, agriculture has played a pivotal role in shaping societies, landscapes, and livelihoods, fostering connections between people and the land they cultivate. In the contemporary world, agriculture faces a myriad of challenges, from population growth and climate change to resource scarcity and ecology degradation. In light of the growing global population, surpassing 8.1 billion people, and climate change alters weather patterns and exacerbates extreme weather events, the pressure on agricultural systems to produce more food while conserving natural resources and mitigating environmental impact has never been more acute. Through the analysis of datasets sourced from the Government of India, encompassing critical factors such as pH, temperature, rainfall, humidity, and NPK substance, the study aims to provide actionable insights for agricultural stakeholders. At the heart of the project lies the recognition that informed decision-making is essential for driving efficiency, resilience, and profitability in agricultural operations. By empowering stakeholders with data-driven predictions and informed strategies, the project aims to enhance agricultural business development, enabling farmers, policymakers, and other stakeholders to make more informed choices. Whether it's optimizing crop selection, improving resource allocation, or mitigating risks associated with climate variability, the project endeavors to provide tools to the stakeholders and managing the complexities of modern life requires knowledge and experience agriculture. The predicted accuracy is 99.69%.

Keyphrases: agriculture sector, machine learning model, random forest algorithm

In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 255-265.

Links:	https://easychair.org/publications/paper/2Wsp
	https://doi.org/10.29007/3bq5

BibTeX entry

@inproceedings{ICSSIT2024:Forecasting_Agricultural_Business_Development,
  author    = {Mahendhiran P D and Aakash V and Arees Muhmmed S and Rithanya S and Varun S},
  title     = {Forecasting Agricultural Business Development based on Seasonal Demand using Machine Learning Techniques},
  booktitle = {Proceedings of 6th International Conference on Smart Systems and Inventive Technology},
  editor    = {Rajakumar G},
  series    = {Kalpa Publications in Computing},
  volume    = {19},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/2Wsp},
  doi       = {10.29007/3bq5},
  pages     = {255-265},
  year      = {2024}}

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