Title: Modified oversampling based borderline SMOTE with noise reduction techniques for IoT smart farming dataset

Authors: M. Suresh; S. Manju Priya

Addresses: Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, India ' School of Computer Science and Applications, REVA University, Bangalore

Abstract: Global population is anticipated to grow exponentially to 10 billion in the future years. To feed the globe, agriculture must be prioritised. Agriculture is vital to human survival. Every field plant breeding, agricultural monitoring, automated maintenance systems, sensor use, and agrochemicals has evolved physiologically and technologically. Technology and analytics merge in Internet of Things (IoT)-based farm data. Machine learning algorithms analyse massive agricultural data. Predictive analytics learning algorithms built with machine learning are fast and effective. The data pipeline's pre-processing stage uses the SMOTE with noise reduction, an advanced oversampling technique. This unique pre-processing method is rigorously compared to SMOTE, ADASYN, and NRAS to assess its efficacy and robustness. This comparison analysis evaluates our improved method's precision, recall, and accuracy in class imbalance scenarios, a common machine learning challenge. To increase synthetic sample quality and model prediction, address dataset noise and borderline occurrences. This research claims that pre-processing affects machine learning models, especially with skewed data. Dataset preprocessing and WSVM performance analysis. The precision, sensitivity, f1-score, accuracy, specificity, and time consumption of MSBNRT are superior.

Keywords: machine learning; IoT smart farming; noise reduction; modified borderline synthetic minority over-sampling technique (SMOTE); data preprocessing.

DOI: 10.1504/IJSSE.2026.152423

International Journal of System of Systems Engineering, 2026 Vol.16 No.1, pp.98 - 120

Received: 08 Aug 2023
Accepted: 29 Nov 2023
Published online: 19 Mar 2026 *

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