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Crop Yield KNN NB DT Streamlit in Python Projects
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Crop Yield KNN NB DT Streamlit in Python Projects
Technical Details
Domain : Python
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
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About This Product
Crop Yield KNN NB DT Streamlit in Python Projects
Abstract
Crop yield prediction is one of the most critical challenges in agriculture, as it directly impacts food security, resource planning, and farmer income. This project, Crop Yield Prediction Using KNN, Naïve Bayes, and Decision Tree with Streamlit in Python, develops a machine learning–based solution that predicts crop yield using soil data, rainfall, temperature, and other environmental features. Three algorithms—K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Decision Tree (DT)—are trained and evaluated for accuracy in predicting crop productivity. The application is deployed using Streamlit, providing farmers and researchers with an interactive web interface for inputting data, visualizing results, and comparing model performance. This system assists in better crop planning, agricultural policy-making, and sustainable farming.
Existing System
Traditional crop yield estimation methods are based on historical averages, manual surveys, or expert knowledge, which are often time-consuming, error-prone, and generalized. Some existing computerized systems rely on simple regression techniques but fail to handle complex, nonlinear relationships between crop yield and multiple influencing factors such as soil fertility, climate variability, and rainfall distribution. Additionally, most existing tools are not interactive or user-friendly, making them inaccessible to small-scale farmers.
Proposed System
The proposed system applies machine learning algorithms (KNN, Naïve Bayes, and Decision Tree) for predicting crop yield and integrates them into a Streamlit-based web application. The workflow includes data preprocessing (cleaning, feature selection, and normalization), model training using KNN (for pattern-based prediction), Naïve Bayes (for probabilistic classification), and Decision Tree (for rule-based analysis). The system allows users to input soil and climate data through the Streamlit interface, after which the models predict the expected yield. Visualizations such as bar charts, accuracy comparisons, and prediction graphs are also provided. Compared to existing systems, this solution offers better accuracy, ease of use, real-time interaction, and multiple algorithm comparisons, making it a reliable tool for farmers and policymakers.
Abstract
Crop yield prediction is one of the most critical challenges in agriculture, as it directly impacts food security, resource planning, and farmer income. This project, Crop Yield Prediction Using KNN, Naïve Bayes, and Decision Tree with Streamlit in Python, develops a machine learning–based solution that predicts crop yield using soil data, rainfall, temperature, and other environmental features. Three algorithms—K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Decision Tree (DT)—are trained and evaluated for accuracy in predicting crop productivity. The application is deployed using Streamlit, providing farmers and researchers with an interactive web interface for inputting data, visualizing results, and comparing model performance. This system assists in better crop planning, agricultural policy-making, and sustainable farming.
Existing System
Traditional crop yield estimation methods are based on historical averages, manual surveys, or expert knowledge, which are often time-consuming, error-prone, and generalized. Some existing computerized systems rely on simple regression techniques but fail to handle complex, nonlinear relationships between crop yield and multiple influencing factors such as soil fertility, climate variability, and rainfall distribution. Additionally, most existing tools are not interactive or user-friendly, making them inaccessible to small-scale farmers.
Proposed System
The proposed system applies machine learning algorithms (KNN, Naïve Bayes, and Decision Tree) for predicting crop yield and integrates them into a Streamlit-based web application. The workflow includes data preprocessing (cleaning, feature selection, and normalization), model training using KNN (for pattern-based prediction), Naïve Bayes (for probabilistic classification), and Decision Tree (for rule-based analysis). The system allows users to input soil and climate data through the Streamlit interface, after which the models predict the expected yield. Visualizations such as bar charts, accuracy comparisons, and prediction graphs are also provided. Compared to existing systems, this solution offers better accuracy, ease of use, real-time interaction, and multiple algorithm comparisons, making it a reliable tool for farmers and policymakers.
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