MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202611087152 A) filed by Uttaranchal University on July 16, 2026, for Ai-Based Soil Health Prediction System.
Inventors include Girija Sankar Joshi; and Sumit Chaudhary.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: An AI-based soil health prediction system and method utilize an edge-to-cloud network architecture to perform real-time diagnostic evaluation of distributed agricultural fields. The system includes a plurality of distributed solar-powered smart sensory data collector devices () (10) and a lesser number of Raspberry Pi-based edge vision data collector devices () (30) scattered across a field plot (). The smart sensory devices continuously measure sub-surface soil moisture, pH, electrical conductivity, temperature, NPK nutrient values, compaction, and dissolved oxygen, while the edge vision devices employ RGB, infrared, thermal, or multispectral cameras (45) to capture canopy features indicating weed presence, leaf discoloration, crop diseases, and surface cracking. Edge processing components perform signal filtering, threshold validation, image sharpening, and compression before transmitting data via low-power LoRa modules (25, 50) to a local gateway unit (40). The gateway unit consolidates, validates, and uploads the packets over a Wi-Fi or internet network link to a centralized cloud server (60). The cloud server executes an automated preprocessing and multimodal data fusion pipeline that merges the tabular sensor records and computer vision features into a uniform dataset. This dataset is evaluated by a pretrained machine learning model (such as Random Forest, XGBoost, or Gradient Boosting combined with CNNs) to predict a soil health index, irrigation requirements, nutrient deficiencies, disease risks, and potential crop yields. An integrated power supply manager tracks solar charging cycles and automatically enters a low-power mode when battery reserves drop below a set threshold. Crucial anomalies and predictive insights are instantly pushed out to web dashboards and mobile notification systems to enable precise field management.
Disclaimer: Curated by HT Syndication.
