Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3948 2019 2019 Assistant Professor, Department of Computer Science and Business Systems, KPR Institute of Engineering and Technology, Coimbatore, India T. T. Assistant Professor, Department of Information Technology, SRMIST, Ramapuram, Chennai, India RM. Rani Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, Telangana, India Gaddam Ravindra Babu Assistant Professor, Department of Information Technology, Aditya University, surampalem, Andhra Pradesh, 533437, India T. Srinivasulu Assistant Professor, Department of Artificial Intelligence and Data Science, St Joseph's Institute of Technology, Chennai, India S. Saranya Assistant Professor, Gnanamani College of Technology, Namakkal, Tamilnadu, India S. Gopinath Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU), Tamilnadu, India M. Rajesh   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. 2026 2026 238 249 10.54216/JISIoT.180118 https://www.americaspg.com/articleinfo/18/show/3948