Crop Yield and Price Prediction

Abstract

Crop yield and price detection are crucial factors in agriculture that affect farmers' income and food production. Machine learning techniques have been increasingly used to predict crop yield and price based on various parameters such as environmental, soil, and crop features. This study proposes a combined approach of using random forest for price detection and decision tree regression for crop yield detection. The model is trained and tested on a large dataset of crop parameters and historical prices. Results indicate that the proposed model outperforms existing methods with an accuracy of 88.5% for price detection and 89.2% for crop yield detection. The model's ability to accurately predict crop yield and price can assist farmers and policymakers in making informed decisions about
crop production and marketing, ultimately improving food security and agricultural sustainability.