Meta-Analysis: Development of Autonomous Agricultural Machinery Systems in Wetland Farming

Aditya Alphanori, tamaria panggabean, Amin Rejo

Abstract


Pertanian lahan basah sangat penting bagi ketahanan pangan global namun juga menghadapi tantangan unik yang menghambat mekanisasi konvensional. Kajian ini menggunakan observasi pustaka sistematis dan analisis bibliometrik untuk pemetaan lanskap penelitian mesin pertanian otonom (ALSINTAN) untuk lahan basah. berikutnya protokol PRISMA, 95 artikel relevan dijelaskan dari tahun 2015-2024. Menghasilkan identifikasi lima klaster penelitian utama: IoT dan pemantauan waktu nyata, kendaraan robotik dan otonom, kecerdasan buatan, kembaran digital, serta ekonomi dan keturunan, dengan pertumbuhan publikasi tahunan sebesar 18,7%. Sebuah temuan penting mengungkapkan bahwa hanya 22% penelitian ALSINTAN otonom yang secara khusus membahas konteks lahan basah, yang menunjukkan bias yang signifikan terhadap penerapan lahan kering. Kesenjangan utama meliputi kinerja sensor dan algoritma yang tidak dapat diandalkan dalam kondisi lahan basah ekstrem dan kurangnya studi adopsi sosial-ekonomi. Studi ini menyimpulkan bahwa pendekatan yang lebih seimbang, yang mengintegrasikan inovasi teknis tingkat lanjut dengan penelitian sosial-ekonomi yang mendalam, sangat dibutuhkan. Hal ini dikonseptualisasikan dalam kerangka kerja Sistem Pertanian Otonom Spesifik Lahan Basah (WSAFS) yang diusulkan untuk mengembangkan sistem pertanian otonom yang tangguh dan inklusif untuk lahan basah.

Keywords


Autonomous Agricultural Machinery; Wetland Farming; Systematic Literature Review; Research Gaps; WSAFS Framework

References


G. Adamides, C. Katsanos, I. Constantinou, G. Christou, M. Xenos, T. Hadzilacos, and Y. Edan, "Design and development of a semi-autonomous agricultural vineyard sprayer: Human-robot interaction aspects," Journal of Field Robotics, vol. 34, no. 8, pp. 1407-1426, Aug. 2017, doi: 10.1002/rob.21721.

D. A. Basterrechea, J. Rocher, M. Parra, L. Parra, J. F. Marin, P. V. Mauri, and J. Lloret, "Design and Calibration of Moisture Sensor Based on Electromagnetic Field Measurement for Irrigation Monitoring," Chemosensors, vol. 9, no. 9, p. 251, Sep. 2021, doi: 10.3390/chemosensors9090251.

F. J. Dessart, J. Barreiro-Hurlé, and R. van Bavel, "Behavioural factors affecting the adoption of sustainable farming practices: A policy-oriented review," European Review of Agricultural Economics, vol. 46, no. 3, pp. 417-471, Jun. 2019, doi: 10.1093/erae/jbz019.

A. R. Dhar, M. M. Islam, A. Jannat, and J. U. Ahmed, "Wetland agribusiness aspects and potential in Bangladesh," Data in Brief, vol. 16, pp. 617-621, Feb. 2018, doi: 10.1016/j.dib.2017.11.055.

D. Durant, A. Farruggia, and A. Trichieur, "Utilization of Common Reed (Phragmites australis) as Bedding for Housed Suckler Cows: Practical and Economic Aspects for Farmers," Resources, vol. 9, no. 12, p. 140, Dec. 2020, doi: 10.3390/resources9120140.

A. Ghobadpour, G. Monsalve, A. Cardenas, and H. Mousazadeh, "Off-Road Electric Vehicles and Autonomous Robots in Agricultural Sector: Trends, Challenges, and Opportunities," Vehicles, vol. 4, no. 3, pp. 843-864, Sep. 2022, doi: 10.3390/vehicles4030047.

Y. He, H. Jiang, H. Fang, Y. Wang, and Y. Liu, "Research progress of intelligent obstacle detection methods of vehicles and their application on agriculture," Transactions of the Chinese Society of Agricultural Engineering, vol. 34, no. 21, pp. 21-32, Nov. 2018, doi: 10.11975/j.issn.1002-6819.2018.21.003.

L. Jiang, B. Xu, N. Husnain, and Q. Wang, "Overview of Agricultural Machinery Automation Technology for Sustainable Agriculture," Agronomy, vol. 15, no. 6, p. 1471, Jun. 2025, doi: 10.3390/agronomy15061471.

K. G. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, "Machine learning in agriculture: A review," Sensors, vol. 18, no. 8, p. 2674, Aug. 2018, doi: 10.3390/s18082674.

S. E. Mohamed, A. A. Belal, S. K. Abd-Elmabod, M. A. El-Shirbeny, A. Gad, and M. B. Zahran, "Smart farming for improving agricultural management," The Egyptian Journal of Remote Sensing and Space Sciences, vol. 24, no. 3, pp. 971-981, Dec. 2021, doi: 10.1016/j.ejrs.2021.08.007.

A. Nasirahmadi and O. Hensel, "Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm," Sensors, vol. 22, no. 2, p. 498, Jan. 2022, doi: 10.3390/s22020498.

M. Padhiary, D. Saha, R. Kumar, L. N. Sethi, and A. Kumar, "Enhancing precision agriculture: A comprehensive review of machine learning and AI vision applications in all-terrain vehicle for farm automation," Smart Agriculture Technology, vol. 8, Dec. 2024, Art. no. 100483, doi: 10.1016/j.atech.2024.100483.

L. Prause, "Digital Agriculture and Labor: A Few Challenges for Social Sustainability," Sustainability, vol. 13, no. 11, p. 5980, May 2021, doi: 10.3390/su13115980.

K. A. Steen, P. Christiansen, H. Karstoft, and N. Jørgensen, "Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture," Journal of Imaging, vol. 2, no. 1, pp. 1-8, Mar. 2016, doi: 10.3390/jimaging2010006.

C. Verdouw, B. Tekinerdogan, A. Beulens, and S. Wolfert, "Digital twins in smart farming," Agricultural Systems, vol. 189, May 2021, Art. no. 103046, doi: 10.1016/j.agsy.2020.103046.

W. Wei, M. Xiao, W. Duan, H. Wang, Y. Zhu, C. Zhai, and G. Geng, "Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields," Agriculture, vol. 14, no. 9, p. 1473, Sep. 2024, doi: 10.3390/agriculture14091473.

S. Wolfert, L. Ge, C. Verdouw, and M. J. Bogaardt, "Big Data in Smart Farming—A review," Agricultural Systems, vol. 153, pp. 69-80, May 2017, doi: 10.1016/j.agsy.2017.01.023.


Refbacks



Copyright (c) 2026 Sriwijaya Journal of Environment

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.