MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641108132 A) filed by Pragati Engineering College on September 09, 2026, for Portable Soil Health Analyzer With Ai-Based Fertilizer Recommendation System.
Inventors include Dr. K Saroja Rani; Dr. Subodh Kumar Panda; Dr. Balla Satyanarayana; and Mr. K. Chakravarthy Bheri.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: Assessing soil health and developing the fertilizer doses are constant issues for farmers globally, especially smallholders in developing agricultural economies who lack real-time access to reliable and affordable soil information. This invention fills those voids with a portable soil analyzer that integrates an Artificial Intelligence system for use in the field and in real-time. The existing solutions have major drawbacks: conventional soil testing in the lab takes days to weeks, often after the best time to apply, hand-held pH and moisture meters leave out the NPK macronutrients, colourimetric chemical test kits allow subjective interpretation errors and require handling of chemicals, satellite and remote-sensing platforms use indirect data that cannot measure field-level variability, and government soil health card schemes test too sparsely and at inappropriate densities to inform field-by-field, season-by-season decisions. Of importance, all of these methods do not integrate multiple parameter sensing with an automatic, crop-specific fertilizer application recommendation into one field-deployable instrument. The limitations of the existing invention are overcome by the provision of a two-part integrated system, in accordance with the proposed invention. The first is a small portable hand-held unit powered by a battery, which has electrochemical and optical sensor probes to measure the pH, moisture, electrical conductivity and NPK concentrations in the soil itself. These readings are measured, calibrated on board and processed by a microcontroller and sent to a paired mobile app using Bluetooth. The second component is an AI/ML-based recommendation engine trained with data sets correlating soil parameters with the crop and regional agronomic data that automatically translates the sensor data and selected crop type into a specific fertilizer type and dosage recommendation that can be implemented as an embedded offline model, or a cloud-based model, or a hybrid of both.
Disclaimer: Curated by HT Syndication.
