MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202641096427 A) filed by Dr. Sivamani P; D. Subhashini; Botla Mamatha; Tejaswini Panse; Dr. V. P. Krishnamurthy; Keerthi Devasenapadhy; Dr. B. Jayanthi; Mr. P. Mariappan; Senthil Kumar D; Gobhinath S; R. Veerappan; and Dinesh Kumar A on August 10, 2026, for An Iot And Machine Learning Enabled Drone System For Precision Crop Monitoring And Management.
Inventors include Dr. Sivamani P; D. Subhashini; Botla Mamatha; Tejaswini Panse; Dr. V. P. Krishnamurthy; Keerthi Devasenapadhy; Dr. B. Jayanthi; Mr. P. Mariappan; Senthil Kumar D; Gobhinath S; R. Veerappan; and Dinesh Kumar A.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: The present invention relates to an IoT and Machine Learning Enabled Drone System for Precision Crop Monitoring and Management, which integrates unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensors, machine learning algorithms, edge computing, cloud computing, and wireless communication technologies to provide intelligent and automated agricultural monitoring. The system employs a drone equipped with RGB, multispectral, and thermal imaging cameras to capture aerial images of crop fields, while distributed IoT sensors continuously monitor soil moisture, soil temperature, humidity, pH, nutrient levels, and environmental conditions. The collected data are transmitted to edge and cloud platforms, where they are preprocessed and analyzed using machine learning algorithms such as Convolutional Neural Networks (CNN), Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), XGBoost, and Long Short-Term Memory (LSTM) networks to assess crop health, detect diseases and pest infestations, predict irrigation requirements, estimate crop yield, and recommend precision farming practices. The invention further enables targeted pesticide and fertilizer spraying through drone-based precision application, thereby optimizing resource utilization and minimizing environmental impact. A cloud-based monitoring platform provides centralized supervision, real-time field visualization, predictive analytics, remote farm management, and decision support. The proposed system enhances crop productivity, reduces labor and operational costs, improves resource efficiency, supports sustainable agriculture, and provides an intelligent and scalable solution for next-generation precision farming.
Disclaimer: Curated by HT Syndication.
