MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202621030139 A) filed by Dr. Swati Gorakshnath Wagh; Suresh Sahadev Kapare; Dr. Jyoti Gorakshnath Wagh; Dr. Ganesh Madhavrao Kangune; and Dr. Vijay Machindra Jadhav, Ahilyanagar, Maharashtra, on March 13, for 'machine learning system for farmer credit evaluation using crop, soil and market data.'
Inventor(s) include Dr. Swati Gorakshnath Wagh; Suresh Sahadev Kapare; Dr. Jyoti Gorakshnath Wagh; Dr. Ganesh Madhavrao Kangune; and Dr. Vijay Machindra Jadhav.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "The present invention discloses a machine learning-based system and method for evaluating farmer creditworthiness using integrated crop, soil, climatic, yield, and agricultural market data. The invention addresses limitations of traditional collateral-based and manually driven credit assessment mechanisms by introducing a data-driven, automated, and adaptive credit scoring framework tailored specifically for agricultural contexts. The system comprises a data acquisition module configured to collect heterogeneous farm-level data from soil testing reports, crop production records, weather databases, historical yield datasets, irrigation details, and commodity market platforms. A preprocessing and feature engineering module transforms the collected data into structured predictive indicators such as soil fertility index, crop profitability ratio, climatic risk coefficient, and market volatility index. A predictive analytics engine employing machine learning algorithms, including regression, classification, and ensemble models, is trained using historical agricultural and loan repayment data to generate a quantitative credit risk score representing repayment probability. The system further includes a dynamic learning mechanism that periodically updates model parameters based on newly available seasonal and market data to maintain prediction accuracy under changing agricultural conditions. A risk categorization and reporting module classifies farmers into predefined risk levels and provides decision-support recommendations for financial institutions. The invention enhances credit accessibility, reduces loan default risk, promotes financial inclusion, and strengthens sustainable rural lending ecosystems through transparent and objective credit evaluation."
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