2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 1 , PP: 238-249, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
T. Shanmugapriya 1 * , RM. Rani 2 , Gaddam Ravindra Babu 3 , T. Srinivasulu 4 * , S. Saranya 5 , S. Gopinath 6 , M. Rajesh 7
Doi: https://doi.org/10.54216/JISIoT.180118
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.
Sustainable agriculture , Field analysis , Cloud IoT model , Remote sensing , Deep learning model
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