Smart Farming: Inovasi Greenhouse Berbasis Ai Untuk Pemberdayaan Petani Krisan Di Desa Tutur

Fikri Bachtiar; Ahmad Zulham Fahamsyah  Havy; Mukhammad Fajrul  Falaq; Taufiqur  Rohman S; Nurul  Faizah; Rizka  Fauziyah; Davi  Sabilissalim; Ucik  Alaida; Setiawan Cahyono  Putro; Muhammad  Syarwani

doi:10.59395/altifani.v6i1.1060

Authors

Fikri Bachtiar University Yudharta Pasuruan
Ahmad Zulham Fahamsyah Havy Universitas Yudharta Pasuruan
Mukhammad Fajrul Falaq Universitas Yudharta Pasuruan
Taufiqur Rohman S Universitas Yudharta Pasuruan
Nurul Faizah Universitas Yudharta Pasuruan
Rizka Fauziyah Universitas Yudharta Pasuruan
Davi Sabilissalim Universitas Yudharta Pasuruan
Ucik Alaida Universitas Yudharta Pasuruan
Setiawan Cahyono Putro Universitas Yudharta Pasuruan
Muhammad Syarwani Universitas Yudharta Pasuruan

DOI:

https://doi.org/10.59395/altifani.v6i1.1060

Keywords:

Smart Farming, Greenhouse AI, Pemberdayaan Petani, Pupuk Organik, Sensor IoT

Abstract

Pengabdian masyarakat ini difokuskan pada penyelesaian isu degradasi kesuburan lahan serta minimnya pemanfaatan teknologi di antara petani bunga krisan di Desa Tutur, Kabupaten Pasuruan. Permasalahan utama mencakup ketergantungan berlebih pada pupuk sintetik, pengairan manual yang boros, serta keterbatasan jangkauan pasar. Pendekatan yang diterapkan adalah Participatory Action Research(PAR) melalui serangkaian tahap, termasuk identifikasi kebutuhan Masyarakat melalui Focus Group Discussion(FGD), penyusunan melalui modul pelatihan, implementasi IoT sensor berbasis kecerdasan buatan (AI) untuk pemantauan tanah dan pengairan otomatis, serta pengembangan platform digital untuk branding produk lokal. Hasil utama menunjukkan peningkatan pemahaman petani mencapai 75%, penurunan penggunaan pupuk kimia hingga 30%, serta kenaikan aktivitas penjualan online hingga 70%. Secara keseluruhan, inisiatif ini berhasil meningkatkan ketahanan ekonomi keberlanjutan, dengan prospek penyebaran ke wilayah lain untuk mendukung pertanian presisi di indonesia.

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References

Artetxe, E., Barambones, O., Martín Toral, I., Uralde, J., Calvo, I., & del Rio, A. (2024). Smart IoT Irrigation System Based on Fuzzy Logic, LoRa, and Cloud Integration. Electronics (Switzerland), 13(10). https://doi.org/10.3390/electronics13101949 DOI: https://doi.org/10.3390/electronics13101949

Chen, H., Zhao, J., Jiang, J., Zhao, Z., Guan, Z., Chen, S., Chen, F., Fang, W., & Zhao, S. (2021). Effects of inorganic, organic and bio-organic fertilizer on growth, rhizosphere soil microflora and soil function sustainability in chrysanthemum monoculture. Agriculture (Switzerland), 11(12). https://doi.org/10.3390/agriculture11121214 DOI: https://doi.org/10.3390/agriculture11121214

Hoque, M. J., Islam, M. S., & Khaliluzzaman, M. (2023). A Fuzzy Logic- and Internet of Things-Based Smart Irrigation System †. Engineering Proceedings, 58(1). https://doi.org/10.3390/ecsa-10-16243 DOI: https://doi.org/10.3390/ecsa-10-16243

Jin, X., Chieng, D., Kar, P., Kwong, C. F., Li, Y., & Wang, Y. (2025). Large-Scale Continuous Monitoring of Greenhouse Gases with Adaptive LoRaWAN in CN470–510 MHz Band. Sensors, 25(17), 1–22. https://doi.org/10.3390/s25175349 DOI: https://doi.org/10.3390/s25175349

Lee, M. H., Yao, M. H., Kow, P. Y., Kuo, B. J., & Chang, F. J. (2024). An Artificial Intelligence-Powered Environmental Control System for Resilient and Efficient Greenhouse Farming. Sustainability (Switzerland), 16(24), 1–18. https://doi.org/10.3390/su162410958 DOI: https://doi.org/10.3390/su162410958

Liu, X., Zhao, Z., & Rezaeipanah, A. (2025). Intelligent and automatic irrigation system based on internet of things using fuzzy control technology. Scientific Reports, 15(1), 1–19. https://doi.org/10.1038/s41598-025-98137-2 DOI: https://doi.org/10.1038/s41598-025-98137-2

Maraveas, C. (2023). Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art. Applied Sciences (Switzerland), 13(1). https://doi.org/10.3390/app13010014 DOI: https://doi.org/10.3390/app13010014

Mgohele, R. N., Massawe, B. H. J., Shitindi, M. J., Sanga, H. G., & Omar, M. M. (2024). Prediction of soil texture using remote sensing data. A systematic review. Frontiers in Remote Sensing, 5(September), 1–18. https://doi.org/10.3389/frsen.2024.1461537 DOI: https://doi.org/10.3389/frsen.2024.1461537

Pathania, S., Dhiman, S. R., Kashyap, B., Kumar, A., Kaushal, R., Gupta, R. K., Saleh, I. A., Okla, M. K., & Elshikh, M. S. (2024). Influence of planting dates and fertilizer modules on yield of chrysanthemum and soil health. BMC Plant Biology, 24(1), 1–17. https://doi.org/10.1186/s12870-024-05241-y DOI: https://doi.org/10.1186/s12870-024-05241-y

Pereira, F. L., de Oliveira, U. A., de Andrade, M. D., Figueiredo, F. C., Santos, B. R., Carvalho, M., & Barbosa, S. (2024). Biosolid as an alternative source of nutrients in chrysanthemum cultivation. Scientific Reports, 14(1), 1–9. https://doi.org/10.1038/s41598-024-66040-x DOI: https://doi.org/10.1038/s41598-024-66040-x

Sari, S. P., Nugroho, I., Hanafie, R., & Suwarta, S. (2022). Towards feasibility and sustainability of chrysanthemum cut flowers farming: Evidence from Pasuruan Regency, Indonesia. Journal of Socioeconomics and Development, 5(1), 115. https://doi.org/10.31328/jsed.v5i1.3631 DOI: https://doi.org/10.31328/jsed.v5i1.3631

Setiawan, Y., Setyo Prayoga, R. A., Nusyura, F., Ardiansyah, M. A., Khoirudin, M. I., & Syaputra, M. R. (2023). Website profile development for digitalization of grass jelly based MSMEs. Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang, 8(3), 367–377. https://doi.org/10.26905/abdimas.v8i3.10650 DOI: https://doi.org/10.26905/abdimas.v8i3.10650

Shin, S. K., Lee, S. J., & Park, J. H. (2025). Prediction of Soil Properties Using Vis-NIR Spectroscopy Combined with Machine Learning: A Review. Sensors, 25(16). https://doi.org/10.3390/s25165045 DOI: https://doi.org/10.3390/s25165045

Sulaiman, Z., Anindita, R., & Muhaimin, A. (2022). Analysis of the Structure, Conduct and Performance of the Chrysanthemum Flower Market in Pasuruan Regency (Study Case in Tutur Village). Habitat, 33(1), 1–12. https://doi.org/10.21776/ub.habitat.2022.033.1.1 DOI: https://doi.org/10.21776/ub.habitat.2022.033.1.1

Zhang, T., Li, Y., & Wang, M. (2024). Prediction of soil organic carbon and total nitrogen affected by mine using Vis–NIR spectroscopy coupled with machine learning algorithms in calcareous soils. Scientific Reports, 14(1), 1–13. https://doi.org/10.1038/s41598-024-73761-6 DOI: https://doi.org/10.1038/s41598-024-73761-6