MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085380 A) filed by Sri Eshwar College Of Engineering on July 11, 2026, for Ai-Based Intelligent Fish Disease Detection And Early Prediction System Using Deep Learning And Multi- Parameter Water Quality Sensors.
Inventors include S Aravind; Mrs. K. Gowthami; Ms. J. Yashwandra; and Dr. R. Babitha Lincy.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: The present invention concerns an AI-Based Intelligent Fish Disease Detection and Early Prediction System Using Deep Learning and Multi-Parameter Water Quality Sensors for improving fish health management system in aquaculture. The invention combines the technologies of Artificial Intelligence (AI), Deep Learning, Computer Vision, Internet of Things (IoT), cloud computing and predictive analytics in one single application that can detect diseases and predict outbreaks in real-time. It includes an image acquisition module, which captures fish images using underwater camera or a smartphone camera; a multi-parameter water quality sensing module, which continuously monitors the environmental parameters such as temperature, pH, dissolved oxygen (DO), ammonia concentration, turbidity, salinity, nitrate, nitrite, electrical conductivity (EC), and water level; and a deep learning module, which performs image analysis using a Convolutional Neural Network (CNN) to detect disease symptoms based on fish images. Data fusion is used to enhance the diagnostic resolution of the image analysis results with real time water quality data through the cloud- based processing platform. A predictive analytics module analyses the past data and environmental trends to make predictions about future disease outbreaks and gives early warnings before outbreaks of serious diseases. The system also offers an automated treatment recommendation, prevention, and water quality improvement plan via the mobile app or web interface. The proposed invention provides continuous monitoring, accurate disease diagnosis, timely prediction of outbreak, remote management of the farm, intelligent decision support which results in reduced fish mortality, minimal economic loss, better aquaculture productivity, and environmentally sustainable approach.
Disclaimer: Curated by HT Syndication.
