An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 6 | Issue - 1 | march 2024
Published
12 April, 2024
The ever-growing demand for food production calls for innovative solutions in agriculture. This research introduces a machine learning-based approach, specifically utilizing logistic regression, to predict optimal crops based on soil and weather conditions. The dataset encompasses crucial attributes including Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, pH, rainfall, with corresponding crop labels. The proposed methodology employs logistic regression, a powerful classification algorithm, to model the relationships between input features and crop types. Through careful feature engineering, the model is fine-tuned to enhance its predictive accuracy. Rigorous evaluation metrics validate the model's performance, ensuring its reliability in real-world applications. Results showcase the logistic regression model's efficacy in accurately predicting suitable crops for given soil and weather parameters. This predictive tool serves as a practical decision support system for farmers, aiding in crop selection and resource allocation. This research contributes to the synergy of machine learning and agriculture, showcasing logistic regression as a valuable tool for crop prediction and resource optimization. As technology continues to transform traditional farming, the integration of logistic regression in precision agriculture offers a practical and efficient approach to crop selection.
KeywordsMachine Learning Logistic Regression Crop Prediction Precision Farming Agricultural Productivity Soil and Weather Parameters
Full Article PDF