Smart City Gnosys

Smart city article details

Title	Ml-Based Yield Prediction In Smart Agriculture Systems Using Iot
ID_Doc	37196
Authors	Prathap C.; Sivaranjani S.; Sathya M.
Year	2024
Published	2024 5th International Conference on Innovative Trends in Information Technology, ICITIIT 2024
DOI	http://dx.doi.org/10.1109/ICITIIT61487.2024.10580172
Abstract	The Internet of Things (IoT) and machine learning (ML) are two of the most rapidly expanding academic areas. 'Smart x' systems that utilize ML and IoT include smart houses, smart cars, smart campuses, smart cities, and early warning systems. The worldwide flow of information between organizations will change as a result of the use of such systems. This research provides a hybrid ML model that utilizes IoT to forecast crop yields. The three main parts of this research are preprocessing, classification, and Smart Campus (SC). Using the Variance Inflation Factor (VIF) technique and correlation-based sensitivity analysis (CBSC), we perform FS after cleaning the data. Last but not least, we propose a two-tiered machine learning architecture for IoT-enabled smart farming. The Adaptive k-Nearest Centroid Neighbor Classifier (aKNCN) model is presented in the first tier to classify soil samples into groups according to the input soil parameters and to calculate the soil quality. Using the Extreme Early Warning System (EWS), the crop yield is forecasted in the second tier. Utilising the Enchance Butterfly Optimisation algorithm (eBOA), the weights are modified in the optimised strategy to increase ELM performance accuracy while minimising error numbers. The application tool for testing the suggested system is Python. The suggested prediction model's performance is assessed using the soil dataset. The performance evaluation considers several parameters, including accuracy, RMSE, R2, MSE, MedAE, MAE, MSLE, MAPE, and Explained Variance Score (EVS). © 2024 IEEE.
Author Keywords	Agriculture; IoT; Machine learning; Python; Sensors; yield prediction

Similar Articles

Id	Similarity	Authors	Title	Published
16601	0.908	Rastog R.; Bhardwaj M.; Sharma A.	Crop And Yield Prediction Through Machine Learning Techniques To Maximize Production: 21St Century Sustainable Approach For Smart Cities 5.0	Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022 (2022)
4881	0.898	Salma M.U.; Rachana S.; Srinivas N.	A Spatio-Temporal Model For The Analysis And Classification Of Soil Using The Iot	Cyber-Physical, IoT, and Autonomous Systems in Industry 4.0 (2021)
42661	0.891	Akhter R.; Sofi S.A.	Precision Agriculture Using Iot Data Analytics And Machine Learning	Journal of King Saud University - Computer and Information Sciences, 34, 8 (2022)
41821	0.888	Gamal A.S.; Mohamed H.K.	Performance Modelling Of Iot In Smart Agriculture	IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023 - Proceedings (2023)
35500	0.882	Amuthasurabi M.; Maaran C.T.; Vyas H.; Prakash R.K.; Arvindhan S.P.; Ashwin B.	Location And Weather-Based Prediction Of Yields Using Machine Learning Algorithms In Smart Cities	Deep Learning and Blockchain Technology for Smart and Sustainable Cities (2025)
57458	0.875	Hayajneh A.M.; Aldalahmeh S.A.; Alasali F.; Al-Obiedollah H.; Zaidi S.A.; McLernon D.	Tiny Machine Learning On The Edge: A Framework For Transfer Learning Empowered Unmanned Aerial Vehicle Assisted Smart Farming	IET Smart Cities, 6, 1 (2024)
16603	0.874	Bhuyan B.P.; Tomar R.; Singh T.P.; Cherif A.R.	Crop Type Prediction: A Statistical And Machine Learning Approach	Sustainability (Switzerland), 15, 1 (2023)
33717	0.871	Ilangaweera I.A.S.; Naramada S.E.M.H.; Sajith N.T.M.; Aashiq M.N.M.; Hinas M.N.A.	Iot Based Smart Plant Growing And Caring Platform For Urban Environments	Proceedings of ICITR 2024 - 9th International Conference on Information Technology Research: Harnessing the Potential of Information Technology (2024)
50885	0.87	Rathore N.; Rajavat A.	Smart Farming Based On Iot-Edge Computing: Applying Machine Learning Models For Disease And Irrigation Water Requirement Prediction In Potato Crop Using Containerized Microservices	Precision Agriculture for Sustainability: Use of Smart Sensors, Actuators, and Decision Support Systems (2024)
50893	0.87	Singh D.; Kumar A.; Chauhan M.	Smart Fields, Smart Yields: Technologies Driving Precision Agriculture Revolution A Survey	Lecture Notes in Networks and Systems, 1037 LNNS (2024)