Authors : Vamsi Krishna; P. Manichandra; Y. Sathya Keerthi; M. Srinivas

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/2crv2dmb

Scribd : https://tinyurl.com/mt2pu2r9

DOI : https://doi.org/10.38124/ijisrt/26jul1616

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Precision agriculture plays a crucial role in enhancing farm productivity, optimizing resource usage, and promoting sustainable farming practices. With the increasing availability of soil data, weather information, and agricultural datasets, large-scale environmental and soil parameters can now be analyzed to make accurate crop recommendations. Traditional crop selection methods, which rely on fixed guidelines or manual observation, often fail to account for the dynamic interactions between soil nutrients, climate variability, and crop responses. This project proposes a Precision Crop Planning System using Machine Learning that assists farmers in making informed decisions about crop selection and farm management. The system takes inputs such as soil nutrient levels (N, P, K), pH, temperature, humidity, rainfall, and soil type to predict the most suitable crop for a given location. Additionally, it provides actionable agricultural guidance, including recommended fertilizers with quantities, irrigation methods, potential diseases for the selected crop, and appropriate pesticides for disease management. The system is integrated into a web-based application with farmer authentication, allowing users to securely login, enter their location and field data, and receive clear, userfriendly, and visually appealing recommendations. The developed system aims to support smarter farming decisions, improved crop yield, and efficient use of resources while minimizing crop losses.

Keywords : Precision Agriculture, Crop Planning, Machine Learning, Soil Analysis, Sustainability, Predictive Modeling, Yield Prediction, Spatial Data, Smart Farming.

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