Year: 2025
Type: article
Abstract: This study proposes a Crop Recommendation System (CRS) using Decision Tree algorithm to provide personalized crop recommendations to farmers. The system utilizes a dataset of soil parameters, climate conditions, and crop characteristics to predict the most suitable crop for a given farm. The Decision Tree algorithm is employed to classify crops based on their suitability for specific soil and climate conditions. The system aims to improve crop yields, reduce resource waste, and promote sustainable agriculture practices. By leveraging machine learning and data analytics, the CRS provides actionable insights to farmers, enabling them to make informed decisions about crop selection. The proposed system has the potential to transform agriculture practices, ensuring food security and sustainability. The results of this study demonstrate the effectiveness of the Decision Tree algorithm in predicting suitable crops, with an accuracy of 90%. Keywords: - Agriculture - Crop Recommendation - Decision Tree Algorithm - Machine Learning - Data Analytics - Precision Farming
DOI: 10.55041/ijsrem43894
Authors: Vinay H N, Nithin Pavithra R B
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4409283267