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Generative AI in Enhancing Hydroponic Nutrient Solution Monitoring

Musawer HakimiComputer Science Department, Samangan University, Samangan, AfganistanI Wayan Aditya SuranataInformation Technology Department, Universitas Pendidikan Nasional, IndonesiaZakirullah EzamComputer Science Faculty, Sayed Jamaluddin Afghani University, Kunar, AfghanistanAbdul Wahid SamadzaiFaculty of Computer Science, Kabul University, AfghanistanWahidullah EnayatOndokuz Mayıs Üniversitesi, Samsun, TürkiyeTamanna QuraishiUniversity of the People, USAAbdul Wajid FazilComputer Science Faculty, Badakhshan University, Badakhshan, Afghanistan

Jurnal Ilmiah Telsinas Elektro Sipil dan Teknik Informasijournal2025en

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Annotatsiya

Generative AI for IoT Hydroponics Monitoring System for Smallholder Farmers in Developing Regions This is in an effort to support AI-based narrative feedback for real-time decision-making with reference to sensor data (TDS/EC, temperature) and plant context-the pertinent data are species and age. The system, therefore, consists of an ESP32 sensor device; a Flutter mobile application; and the cloud services being offered via Thingsboard and the Gemini API. A systematic approach was undertaken, including design, implementation, integration, and usability testing. The results show effective real-time data collection and secure communication, with accurate AI feedback validated by expert judgment. The results exhibited how AI and IoT could collude in aiding smart agriculture. Future work will concentrate on enhancing the accuracy of the model based on ground truth data and improving the accessibility of the platform.

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Mavzular

Smart Agriculture and AI Water Quality Monitoring Technologies

Identifikatorlar

DOI: 10.38043/telsinas.v8i1.6242

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