Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Das, Vishal; George, Jossy; Kumar, Pawan Title A name given to the resource Early Detection of Plant Diseases Using IoT Sensors and Machine Learning Algorithms Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.344-350 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00070" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00070</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037448966?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037448966?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Das V., CHRIST (Deemed to be University), India; George J., CHRIST (Deemed to be University), India; Kumar P., CHRIST (Deemed to be University), India Description An account of the resource Agriculture is one of the most important and necessitates of the world. This paper is a study to detect plant diseases using IoT sensors and ML Algorithms for early detection of plant diseases using IoT sensors and machine learning algorithms. A primary dataset from Indian Agricultural Research Institute (ICAR) was used for the research. The dataset comprised the following features: temperature, humidity, soil moisture, leaf wetness, and dew point. Five different machine learning algorithms were explored for the implementation: Logistic Regression, Random Forest, XGBoost, CatBoost, and LightGBM. Upon comparative analysis, it was found that the LightBGM model performed the best with an accuracy of 93.4 % using cross-validation, implying remarkable performance for real-time plant disease monitoring. 2025 IEEE. Subject The topic of the resource CatBoost; Gradient Boosting; Internet of Things (IoT); Machine Learning; Plant Disease Detection; Precision Agriculture Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online