MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202621073028 A) filed by Sanjeevkumar Angadi; Dr. Bhavana R Maale; Mrs. Saili Sable; Mrs. Tejaswini Zope; Mrs. Pranita Kishor Kachare; Mrs. Snehal Sarangi; Mrs. Pooja Sammer Bhondve; Mrs. Reshma Kohad; Mrs. Ashwini Basavraj Utture; and Dr. Bharati Kale on June 12, 2026, for System And Method For Smart Agriculture Using Iot Sensors And Cloud-Based Deep Learning For Automated Crop Management.
Inventors include Sanjeevkumar Angadi; Dr. Bhavana R Maale; Mrs. Saili Sable; Mrs. Tejaswini Zope; Mrs. Pranita Kishor Kachare; Mrs. Snehal Sarangi; Mrs. Pooja Sammer Bhondve; Mrs. Reshma Kohad; Mrs. Ashwini Basavraj Utture; and Dr. Bharati Kale.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: The present invention discloses a system and method for smart agriculture that integrates Internet of Things (IoT) sensors with cloud-based deep learning techniques to enable real-time monitoring, predictive analytics, and automated crop management. The proposed system comprises a distributed network of IoT sensors configured to continuously acquire multi-modal agricultural data, including soil moisture, temperature, humidity, pH levels, nutrient content, and environmental conditions. The collected data is transmitted through a communication network to a cloud computing platform for storage, preprocessing, and analysis. The cloud platform incorporates a deep learning module utilizing advanced neural network architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to perform feature extraction, temporal analysis, and predictive modeling. The system is capable of identifying crop health status, detecting diseases at early stages, estimating irrigation requirements, and forecasting yield based on real-time and historical data. The deep learning models are designed to continuously learn and adapt from incoming data streams, thereby improving prediction accuracy and system performance over time. A decision-making engine is integrated within the cloud infrastructure to generate prescriptive actions based on model outputs and predefined agronomic rules. The system further includes an actuation module that automatically controls agricultural devices such as irrigation systems, fertilizer dispensers, and pest control mechanisms in response to the generated decisions. This enables a closed-loop automated agricultural management system with minimal human intervention. The invention further supports scalability and interoperability by allowing integration with edge devices and heterogeneous sensor networks. Additionally, the system ensures efficient resource utilization by optimizing water, fertilizer, and pesticide usage, thereby reducing operational costs and environmental impact. The proposed system provides a comprehensive solution for precision agriculture by combining real-time sensing, intelligent cloud-based analytics, and automated control mechanisms, resulting in enhanced crop productivity, sustainability, and decision-making efficiency.
Disclaimer: Curated by HT Syndication.
