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| Natura: | Recurso digital |
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Zenodo
2025
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| Accesso online: | https://doi.org/10.5281/zenodo.16617234 |
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Sommario:
- <p><a href="https://ijetrm.com/issues/files/Jul-2025-30-1753894940-JULY141.pdf">Agriculture, as one of the oldest and most fundamental human activities</a>, plays a vital role in ensuring<br>food security and supporting economies globally. However, modern agricultural practices face several challenges,<br>including climate change, resource depletion, and variable environmental conditions, all of which can impact<br>crop yields. Consequently, accurate prediction of crop yields has become an essential tool in optimizing<br>agricultural practices, ensuring sustainable food production, and maximizing farm productivity.<br>In this context, the Prediction of Crop Yield has emerged as a critical area of research, particularly with the<br>advent of advanced machine learning techniques. These techniques can help provide more precise estimates<br>by analyzing the relationship between various environmental, soil, and crop factors.<br>This project presents a Crop Yield Prediction System, designed to leverage machine learning algorithms,<br>specifically the Decision Tree model, to predict agricultural yields based on key soil and environmental<br>parameters. The system is web-based and developed using the Flask framework, making it accessible and easy<br>to use for farmers, agricultural researchers, and industry professionals.<br>The primary objective of this system is to automate the yield prediction process, enabling users to input essential<br>agricultural parameters such as soil pH, temperature, humidity, wind speed, and nutrient levels (Nitrogen,<br>Phosphorus, Potassium). The application processes these inputs through data preprocessing techniques,<br>including numerical transformation and one-hot encoding, which align the data with the model's required<br>structure.</p>