| _version_ | 1866901101956038656 |
|---|---|
| author | Rudrani Girish Jangale , Riya Antha , Srushti Bhaskar Khatale , Srushti Bajirao Kshirsagar |
| author_facet | Rudrani Girish Jangale , Riya Antha , Srushti Bhaskar Khatale , Srushti Bajirao Kshirsagar |
| contents | <h2>Abstract</h2> <div>Crop price prediction plays a vital role in today’s agriculture, impacting everything fromfarmer profits to market stability and even policy decisions. With the increasing complexity of agricultural systems—thanks to unpredictable weather, varying soil types, regional demands, and global trade there’sa real need for sophisticated computational models to make accurate forecasts. Harvest Horizonintro- duces a data-driven framework that leverages machine learning (ML) and deep learning (DL) techniques, all built on a scalable PySpark-based preprocessing pipeline. The project employs a variety of models, including Convolutional Neural Networks (CNN),Long Short-Term Memory (LSTM) networks,Random Forest (RF), and XGBoost, to delve into the temporal, spatial, and nonlinear relationships foundin agricultural data. By tapping into historical price data, climate factors, and regional specifics, the system can predict crop prices tailored to specific areas and display the findings through an interactive dashboard. The experimental results show that hybrid deep learning models surpass traditional machine learning methods in terms of both accuracy and flexibility. This study underscores the promise of artificial intelligence in agricultural analytics, offering valuable insights for farmers, traders, and policymakers alike.</div> <h2><span></span>Keywords</h2> <div>Keywords:Agricultural Price Fore- casting , Data Analytics in Agriculture, Ma- chine Learning Prediction Models,Time Series Forecasting,Crop Market Value Prediction, Data- Driven Farming Decisions,Azure Machine Learning Studio, Big Data in Agriculture,Market Trend Analysis,Regression Analysis, Predictive Analytics, Agriculture Data Modeling,Farmer Decision Support System,Data Preprocessing and Cleaning,Data Visualization for Agriculture Markets,Commodity Price Analysis, Historical Crop Price Data,Agricultural Data Forecasting Frame- work.</div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18070212 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Harvest Horizon: Data Driven Decisions in Farming Market Pricing Rudrani Girish Jangale , Riya Antha , Srushti Bhaskar Khatale , Srushti Bajirao Kshirsagar <h2>Abstract</h2> <div>Crop price prediction plays a vital role in today’s agriculture, impacting everything fromfarmer profits to market stability and even policy decisions. With the increasing complexity of agricultural systems—thanks to unpredictable weather, varying soil types, regional demands, and global trade there’sa real need for sophisticated computational models to make accurate forecasts. Harvest Horizonintro- duces a data-driven framework that leverages machine learning (ML) and deep learning (DL) techniques, all built on a scalable PySpark-based preprocessing pipeline. The project employs a variety of models, including Convolutional Neural Networks (CNN),Long Short-Term Memory (LSTM) networks,Random Forest (RF), and XGBoost, to delve into the temporal, spatial, and nonlinear relationships foundin agricultural data. By tapping into historical price data, climate factors, and regional specifics, the system can predict crop prices tailored to specific areas and display the findings through an interactive dashboard. The experimental results show that hybrid deep learning models surpass traditional machine learning methods in terms of both accuracy and flexibility. This study underscores the promise of artificial intelligence in agricultural analytics, offering valuable insights for farmers, traders, and policymakers alike.</div> <h2><span></span>Keywords</h2> <div>Keywords:Agricultural Price Fore- casting , Data Analytics in Agriculture, Ma- chine Learning Prediction Models,Time Series Forecasting,Crop Market Value Prediction, Data- Driven Farming Decisions,Azure Machine Learning Studio, Big Data in Agriculture,Market Trend Analysis,Regression Analysis, Predictive Analytics, Agriculture Data Modeling,Farmer Decision Support System,Data Preprocessing and Cleaning,Data Visualization for Agriculture Markets,Commodity Price Analysis, Historical Crop Price Data,Agricultural Data Forecasting Frame- work.</div> |
| title | Harvest Horizon: Data Driven Decisions in Farming Market Pricing |
| url | https://doi.org/10.5281/zenodo.18070212 |