A Machine Learning-Enabled System for Crop Recommendation
<b>Context:</b> We are advancing our efforts in agriculture by creating a crop prediction system with the help of machine learning. Our goal is to build an ML model that can estimate the properties of a crop. It will push ahead in agriculture by developing a predictive tool for crops usi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/67/1/51 |
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| Summary: | <b>Context:</b> We are advancing our efforts in agriculture by creating a crop prediction system with the help of machine learning. Our goal is to build an ML model that can estimate the properties of a crop. It will push ahead in agriculture by developing a predictive tool for crops using machine learning in agriculture in terms of both time and money. Our farmers can understand easily and analyze what best they are going to farm. <b>Objective:</b> The main theme of this project is to support farmers in yielding a good crop by making a robust model. By identifying the significant role of technology in advanced farming practices, we aim to create a solution that helps farmers make informed decisions about crop selections and agricultural practices. Utilizing data analytics and AI-driven insights enhances productivity and efficiency. Our final goal is to encourage farmers with the tools and knowledge they need to grow in an increasingly complex agricultural landscape. <b>Methods</b>: To complete this model, we collected data from different sources like the data of weather, humidity, pH value, temperature, nitrogen, phosphorous, and potassium values, and rainfall in mm. We implemented it through ML algorithms like GNB (Gaussian Naïve Bayes), SVM (Support Vector Machine), RF (Random Forest), and DT (Decision Tree). <b>Result:</b> The GNB classifier achieves an accuracy of 99%, surpassing others. |
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| ISSN: | 2673-4591 |