Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification A. Reyana, Sandeep Kautish, P. M. Sharan Karthik, Ibrahim Ahmed Al-Baltah, Muhammed Basheer Jasser, Ali Wagdy Mohamed IEEE Access, 2023 Sensors are now used by farmers and agronomists to help them improve their operations. They use sensor data transmitted via IoT to remotely monitor their crops. Farmers today manage crops in a controlled environment to increase yields in the name of modern farming. Crop productivity, on the other hand, is influenced by the severity of the weather and disease variations. The primary objective of this paper is to present a novel Multisensor Machine-Learning Approach (MMLA) for classifying multisensor data. The fusion strategy supports high-quality data analysis in agricultural contexts for cultivation recommendations. Based on the proposed recommendation system, eight crops were classified: cotton, gram, groundnut, maize, moong, paddy, sugarcane, and wheat. Crop species were classified using three machine learning algorithms: J48 Decision Tree, Hoeffding Tree, and Random Forest. To evaluate the performance of the proposed multi-text classifier, only the top eight classes were investigated. The classifier’s performance is measured in terms of precision, recall, F-measure, MCC, ROC Area, and PRC Area class, and the results are compared with the state-of-the-art classifiers. The Random forest algorithm has the lowest error measure of RMSE at 13%, RAE at 38.67%, and RRSE at 44.21%, demonstrating effectiveness in classifying the agriculture text. Thus, the use of a multisensor data fusion approach based on crop recommendation provides greater precision in prediction, resulting in a significant increase in crop yield while also creating awareness in the condition-based environmental monitoring system.
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Accelerating crop yield: multisensor data fusion and machine learning for agriculture text classification A Reyana, S Kautish, PMS Karthik, IA Al-Baltah, MB Jasser, ... IEEE Access 11, 20795-20805 , 2023 2023 Citations: 101
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Accelerating crop yield: multisensor data fusion and machine learning for agriculture text classification A Reyana, S Kautish, PMS Karthik, IA Al-Baltah, MB Jasser, ... IEEE Access 11, 20795-20805 , 2023 2023 Citations: 101
Effectiveness of industrial waste materials used as ingredients in fly ash brick manufacturing T Balasubramaniam, PMS Karthik, S Sureshkumar, M Bharath, M Arun Materials Today: Proceedings 45, 7850-7858 , 2021 2021 Citations: 37
Investigation on Causes of Landslides and Mitigation Measures using SPSS PMD Karthik, S Chavan, A Thoke, BS Ganesan, R Honnalli, PMS Karthik Nanotechnology Perceptions 20 (7) , 2024 2024
The Role of IoT in Disaster Management: Building Services and it's Potential for Reuse PMSK Selvabharathi Gopal, C.Merlin Rani International Conference on "Research Trends in AI and IoT for … , 2024 2024
