MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202621069904 A) filed by Symbiosis International Deemed University on June 03, 2026, for System And Method For Machine Learning Based Crop Yield Prediction Using Climate And Soil Parameters.
Inventors include Dr. Harshala Shingne; Kavish Khuje; Kanishk Sontakke; and Ayush Dwivedi.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: ABSTRACT SYSTEM AND METHOD FOR MACHINE LEARNING BASED CROP YIELD PREDICTION USING CLIMATE AND SOIL PARAMETERS The present invention discloses machine learning based crop yield prediction system (100) and method for predicting agricultural crop yield using climate and soil parameters. The system (100) comprises data acquisition module (110) configured to collect agricultural datasets including temperature, rainfall, humidity, soil type, crop type, season, and cultivation area from multiple Indian states and districts. A data preprocessing module (120) includes an ordinal encoding unit (122) for independently encoding categorical variables to eliminate inconsistencies associated with shared label encoders of conventional systems. A model training and evaluation module (130) trains and compares multiple regression algorithms including linear regression, decision tree, random forest, support vector regression, gradient boosting, AdaBoost, and XGBoost. A hyperparameter optimization module (140) performs grid search cross validation for model tuning. A prediction interface module (150) deploys optimized model as an API. The tuned XGBoost model achieves R-squared accuracy of 0.8846 for real time crop yield prediction applications. [
Disclaimer: Curated by HT Syndication.
