MUMBAI, India, June 30 -- Intellectual Property India has published a patent application (202641036709 A) filed by Rmk Engineering College on March 26, 2026, for A Deep Learning Based Precision Farming Advisory System With Secure Sensor Data Authentication Protocol.
Inventors include B. Mythili; Dr. G. Manikandan; Dr. B. Muthazhagan; E. Sathesh Abraham Leo; E. Thenmozhi; B. Saratha; and R. Devi.
The application for the patent was published on June 26, 2026, under issue no. 26/2026.
Abstract: Precision agriculture has emerged as a transformative approach to improve agricultural productivity and sustainability through the use of advanced technologies. The present invention proposes a Deep Learning Based Precision Farming Advisory System with a Secure Sensor Data Authentication Protocol designed to assist farmers in making intelligent and data-driven farming decisions. The system integrates Internet of Things (IoT) sensors, edge computing, deep learning models, and secure communication mechanisms to collect, process, and analyze agricultural data in real time. By continuously monitoring environmental and soil parameters, the system enables precise crop management and optimized resource utilization.The proposed system consists of multiple IoT sensors deployed across agricultural fields to monitor parameters such as soil moisture, temperature, humidity, soil pH, nutrient levels, and light intensity. These sensors transmit real-time data to an edge processing unit, which performs preliminary data filtering and formatting. To ensure data reliability and security, a secure sensor data authentication protocol is implemented using cryptographic techniques such as hash functions, digital signatures, or secure authentication keys. This mechanism verifies the authenticity and integrity of sensor data before it is transmitted to the cloud infrastructure for further processing.The authenticated data is then stored and processed within a cloud computing platform where deep learning models analyze patterns and generate predictive insights. Various machine learning and deep learning techniques such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks are employed to analyze agricultural conditions, predict crop health, estimate yield, and detect potential diseases or environmental stress factors. Based on the analysis, the system generates intelligent recommendations including irrigation scheduling, fertilizer management, and pest or disease alerts.The final advisory information is delivered to farmers through a user-friendly mobile or web- based interface, enabling real-time monitoring and decision support. By combining secure data authentication with deep learning-based analytics, the proposed system enhances the reliability of agricultural data and provides precise farming recommendations. The invention significantly contributes to improved crop productivity, efficient water and fertilizer usage, and sustainable agricultural practices. Keywords Precision Agriculture, Deep Learning, IoT Sensors, Secure Data Authentication, Smart Farming, Crop Prediction, Agricultural Advisory System
Disclaimer: Curated by HT Syndication.
