Agriculture Crop Recommendation Based On Productivity And Seasons

Authors

  • Ankur Tomar
  • Subhajit Ghosh

Keywords:

Farming, Crop Suggestion, Seasons, Productivity, Machine Learning, Meerut

Abstract

The crop recommendation model was trained using a structured dataset containing key agronomic and environmental parameters. As shown in Figure 3, the dataset includes nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, rainfall, and soil pH as input features. The output label indicates the most suitable crop for the given set of conditions. This data forms the foundation for developing the machine learning model used in this study.  In Meerut, many people are farmers. Farming is how they grow food and earn money. But to grow more food, it is very important to choose the right crop for the right season. Sometimes, farmers don’t know which crop is best to grow in the weather and soil of their area. We collected old data about crops, soil, and weather in Meerut. Then we used smart computer programs to study this data and find out which crops grow best in each season. Our system gives suggestions to farmers about the best crop to grow in each season. This helps them grow more food with less effort and earn more money. It makes farming simpler and better for the people of Meerut.

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References

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Published

2025-08-13

How to Cite

1.
Tomar A, Ghosh S. Agriculture Crop Recommendation Based On Productivity And Seasons. J Neonatal Surg [Internet]. 2025Aug.13 [cited 2025Oct.4];14(32S):7402-1. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/8866

Issue

Vol. 14 No. 32S (2025): Journal of Neonatal Surgery

Section

Original Article

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