Intellectual Property India Publishes Patent Application for Machine Learning And Mathematical Modelling-Based Device For Soil Fertility Prediction And Crop Recommendation

MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641090080 A) filed by V. Sabitha; Dumpa Sreepal; Ramanjaneyulu Seggem; Dr. Vijayalakshmi J; Pavan Kumar Kolluru; Dr Manikandan S; Dr. M. Ariyanatchi; Dr. Augustina Saha; Dr. Papita H Gourkhede; Ms. S. Sivaselvi; Dr. A. K. Shrivastav; and Surekha Annappa Khot on July 24, 2026, for Machine Learning And Mathematical Modelling-Based Device For Soil Fertility Prediction And Crop Recommendation.

Inventors include V. Sabitha; Dumpa Sreepal; Ramanjaneyulu Seggem; Dr. Vijayalakshmi J; Pavan Kumar Kolluru; Dr Manikandan S; Dr. M. Ariyanatchi; Dr. Augustina Saha; Dr. Papita H Gourkhede; Ms. S. Sivaselvi; Dr. A. K. Shrivastav; and Surekha Annappa Khot.

The application for the patent was published on August 21, 2026, under issue no. 34/2026.

Abstract: Machine Learning and Mathematical Modelling-Based Device for Soil Fertility Prediction and Crop Recommendation is the proposed invention. The proposed invention discloses a machine learning and mathematical modelling-based device for soil fertility prediction and crop recommendation. The device integrates soil sensing modules, environmental sensors, data acquisition electronics, and an intelligent computational engine to evaluate soil quality in real time. Soil parameters including pH, moisture, temperature, electrical conductivity, organic carbon, nitrogen, phosphorus, potassium, and micronutrients are collected and processed using data preprocessing techniques. An Extreme Gradient Boosting (XGBoost) model predicts soil fertility and nutrient deficiencies, while a mathematical Soil Fertility Index model quantifies overall soil health using weighted nutrient balance equations. A multi-objective optimization framework identifies the most suitable crops by considering soil fertility, climatic conditions, irrigation availability, fertilizer requirements, and expected yield. The device generates customized crop recommendations, fertilizer schedules, and nutrient management strategies through an integrated display or wireless communication interface. Continuous learning from newly acquired field data enhances prediction accuracy over time. The invention enables precision agriculture by improving resource utilization, increasing crop productivity, reducing cultivation costs, and supporting environmentally sustainable farming practices.

Disclaimer: Curated by HT Syndication.