Scalable Mesh Networking with Machine Learning for Real-Time Crop Yield Prediction in Resource-Constrained Agricultural Environments

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dc.contributor.authorAgha, Janib
dc.contributor.authorWamiq, Shehzada
dc.contributor.authorSarioglu, Baykal
dc.date.accessioned2026-04-04T18:48:34Z
dc.date.available2026-04-04T18:48:34Z
dc.date.issued2025
dc.description2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025 -- 10 September 2025 through 12 September 2025 -- Bursa -- 214381
dc.description.abstractThis paper presents a custom-developed, ultra-lowpower mesh network of sensor nodes designed to log environmental parameters such as temperature and humidity across agricultural fields. The system employs a self-organizing, energy-efficient communication framework optimized for long-term deployment in resource-constrained environments. Logged data is combined with historical agricultural datasets to train a Random Forest Regressor specifically tailored for crop yield prediction. The model demonstrates high accuracy and robustness, effectively translating environmental trends into actionable forecasts. This integrated approach offers a scalable, low-cost pathway toward data-informed agricultural planning, enabling farmers to better anticipate outcomes and adapt to evolving climate conditions. A Random Forest Regressor trained on both field and historical data achieved an R2 of 0.98 and MAE of 4770.27 Hg/ Ha, outperforming linear and decision tree models. Real-time sensor data from 2025 was used to generate accurate yield predictions, demonstrating the system's viability for scalable, data-driven precision agriculture. © 2025 IEEE.
dc.identifier.doi10.1109/ASYU67174.2025.11208333
dc.identifier.isbn979-833159727-6
dc.identifier.scopus2-s2.0-105022434054
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ASYU67174.2025.11208333
dc.identifier.urihttps://hdl.handle.net/11411/10231
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260402
dc.subjectCrop Yield Prediction
dc.subjectEsp32
dc.subjectLow Power
dc.subjectMachine Learning
dc.subjectSmart Agriculture
dc.titleScalable Mesh Networking with Machine Learning for Real-Time Crop Yield Prediction in Resource-Constrained Agricultural Environments
dc.typeConference Paper

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