Cloud IoT with Remote Sensing Data Segmentation and Classification Using Deep Learning Model for Sustainable Agriculture

 

 

 

T. Shanmugapriya1,*, RM. Rani2, Gaddam Ravindra Babu3, T. Srinivasulu4, S Saranya5, S. Gopinath6,
M. Rajesh7

 

1Assistant Professor, Department of Computer Science and Business Systems, KPR Institute of Engineering and Technology, Coimbatore, India

 

2Assistant Professor, Department of Information Technology, SRMIST, Ramapuram, Chennai, India

 

3Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, Telangana, India

 

4Assistant Professor, Department of Information Technology, Aditya University, surampalem, Andhra Pradesh,

 

533437, India

 

5Assistant Professor, Department of Artificial Intelligence and Data Science, St Joseph's Institute of Technology, Chennai, India

 

6Assistant Professor, Gnanamani College of Technology, Namakkal, Tamilnadu, India

 

7Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU), Tamilnadu, India

 

Emails: priyamoons@gmail.com; ranir@srmist.edu.in; ravindragaddam@gmail.com; tsrinu531@gmail.com;

 

Text Box: Abstract Sustainable Development Goals of United Nations are focused on enhancing agricultural production that has the potential to be transformational at the local as well as the global level. The available technologies in agriculture management that are based on Internet of Things (IoT) encourage sustainable production of more food by farmers, which contributes significantly to the achievement of these SDGs. The aim of this research is to propose novel technique in sustainable agriculture field analysis based on cloud IoT model with remote sensing and deep learning model. Here the cloud IoT model is used in agriculture field based remote sensing data analysis. This image has been segmented using watershed K-means temporal neural network (WKMTNN) and classification is carried out using deep quantile regressive Boltzmann machine (DQRBM). The experimental analysis has been carried out in terms of random accuracy, average precision, sensitivity, specificity for various agriculture field dataset. Proposed model attained average precision 96%, sensitivity 93%, random accuracy 98%, and Specificity 95%. These results highlight the superiority of the moisture estimation framework against their regression-based counterparts. ssaranrahul@gmail.com; sgopicse@gmail.com; rajesmano@gmail.com

 

Received: February 28, 2025 Revised: June 01, 2025 Accepted: July 10, 2025

 

Keywords: Sustainable agriculture; Field analysis; Cloud IoT model; Remote sensing; Deep learning model