Drone-Assisted Climate Smart Agriculture (DACSA): The design of the groundwork flow data for drone operations

G. S.  Prabowo; A. S. Budiyanta; A.P. Adi; A. Wirawan; H. Mardikasari; F. S. Pranoto; T. K. Wardana; D. Kusumoaji; I. Rismayanti; A. Septiyana; A. Aziz; B. H. Trisasongko

doi:10.4108/eetsis.6923

Authors

G. S. Prabowo National Research and Innovation Agency
A. S. Budiyanta National Research and Innovation Agency
A.P. Adi National Research and Innovation Agency
A. Wirawan National Research and Innovation Agency
H. Mardikasari National Research and Innovation Agency
F. S. Pranoto National Research and Innovation Agency
T. K. Wardana National Research and Innovation Agency
D. Kusumoaji National Research and Innovation Agency
I. Rismayanti National Research and Innovation Agency
A. Septiyana National Research and Innovation Agency
A. Aziz National Research and Innovation Agency
B. H. Trisasongko IPB University

DOI:

https://doi.org/10.4108/eetsis.6923

Keywords:

Ground Support, Precision Farming, Drone, Flow Data, Climate Smart Agriculture

Abstract

The success of precision farming hinges on effective ground support and workflow. In pursuit of this, we undertook a thorough requirement study of the system necessary for precision farming and developed a precision farming data flow model in ground support. The prototype hardware ground support and conceptual data flow provided valuable guidance in the successful realization of Drone-Assisted Climate Smart Agriculture (DACSA). Using open-source software to accommodate a range of data processing algorithms becomes crucial in operationalizing ground support for precision farming. This study has culminated in a comprehensive prototype model for precision farming operations that can be executed with confidence. The management system of flow data for precision farming has been drawn, this platform is specifically crafted to streamline agriculture operations by transforming diverse inputs into useful spatial data. To maintain the growth of the database, it is necessary to incorporate it in the entire crop cycle. The integration of this database can significantly enhance the precision of predicting plant performance. While this innovative approach is still in progress, it has already demonstrated its potential in supporting informed decision-making. For the next, it is imperative that we prioritize research aimed at creating decision-support algorithms that can effectively gather and blend information pertaining to soil, crops, and weather into actionable maps. These maps must incorporate location-specific data and be utilized by agricultural professionals for on-site decision-making. Moreover, they must be well-suited for drone usage in tasks such as monitoring, mapping, or spraying.

References

[1] Watts AC, Ambrosia VG, Hinkley EA. Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use. Remote Sens. 2012;4(6):1671–92.

[2] Radoglou-Grammatikis P, Sarigiannidis P, Lagkas T, Moscholios I. A compilation of UAV applications for precision agriculture. Comput Networks [Internet]. 2020;172(January):107148. Available from: https://doi.org/10.1016/j.comnet.2020.107148

[3] Rejeb A, Abdollahi A, Rejeb K, Treiblmaier H. Drones in agriculture: A review and bibliometric analysis. Comput Electron Agric [Internet]. 2022;198(May):107017. Available from: https://doi.org/10.1016/j.compag.2022.107017

[4] Bollas N, Kokinou E, Polychronos V, 2021, Comparison of sentinel-2 and uav multispectral data for use in precision agriculture: An application from northern greece, Drones, 5(2), doi:10,3390/drones5020035

[5] Bukowiecki J, Rose T, Kage H, 2021, Sentinel-2 data for precision agriculture?—a uav-based assessment, Sensors, 21(8), doi:10,3390/s21082861

[6] Naji I. The Drones’ Impact On Precision Agriculture. 2019;17–23. Available from: https://digitalcommons.utep.edu/open_etd

[7] Shafi U, Mumtaz R, García-Nieto J, Hassan SA, Zaidi SAR, Iqbal N. Precision agriculture techniques and practices: From considerations to applications. Sensors (Switzerland). 2019;19(17):1–25.

[8] Prabowo GS, Wirawan A, Pandjaitan L, Kusuma T, Firmansyah Y, Aziz A, et al. Drone for Precision Farming ( DPF ): Conceptual design , system integration and its preliminary outcomes. First Int Conf Food Agric Sci 2022. 2022;

[9] Akhter R, Sofi SA. Precision agriculture using IoT data analytics and machine learning. J King Saud Univ - Comput Inf Sci [Internet]. 2022;34(8):5602–18. Available from: https://doi.org/10.1016/j.jksuci.2021.05.013

[10] Leroux C, Jones H, Pichon L, Guillaume S, Lamour J, Taylor J, et al. GeoFIS: An open source, decision-support tool for precision agriculture data. Agric. 2018;8(6).

[11] Demestichas K, Daskalakis E. Data lifecycle management in precision agriculture supported by information and communication technology. Agronomy. 2020;10(11).

[12] Reinecke M, Prinsloo T, 2017 Agu 23, The influence of drone monitoring on crop health and harvest size, 2017 1st Int Conf Next Gener Comput Appl NextComp 2017,

[13] Neupane K, Baysal-Gurel F, 2021, Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review, Remote Sens 2021, Vol 13, Page 3841, 13(19):3841, doi:10,3390/RS13193841,

[14] Hafeez A, Husain MA, Singh SP, Chauhan Anurag, Khan MT, Kumar N, Chauhan Abhishek, Soni SK, 2022 Feb 15, Implementation of drone technology for farm monitoring & pesticide spraying: A review, Inf Process Agric,.

[15] Jonathan McFadden, Eric Njuki and TG. Precision Agriculture in the Digital Era: Recent Adoption on U.S. Farms, EIB-248, [Internet]. Available from: https://www.ers.usda.gov/webdocs/publications/105894/eib-248.pdf

[16] Belcore E, Angeli S, Colucci E, Musci MA, Aicardi I. Precision agriculture workflow, from data collection to data management using FOSS tools: An application in Northern Italy vineyard. ISPRS Int J Geo-Information. 2021;10(4).

[17] S BD. USING PRECISION TECHNOLOGY IN ON-FARM FIELD TRIALS TO ENABLE DATA-INTENSIVE FERTILIZER MANAGEMENT [Internet]. 2022. Available from: https://portal.nifa.usda.gov/web/crisprojectpages/1008818-using-precision-technology-in-on-farm-field-trials-to-enable-data-intensive-fertilizer-management.html

[18] What are the main challenges of adopting precision agriculture technologies? [Internet]. Agribussines; 2023. Available from: https://www.linkedin.com/advice/3/what-main-challenges-adopting-precision-agriculture

[19] Aleksandr Sakal. Challenges in the implementation of ag tech today. AGdayli [Internet]. Available from: https://www.agdaily.com/technology/challenges-in-the-implementation-of-ag-tech-today/

[20] Mizik T. How can precision farming work on a small scale? A systematic literature review. Precis Agric. 2023;24(1):384–406.

[21] Yoder M. Four Weather Factors for Plant Growth. 2014.

[22] Dr. Ashish Agarwal. Role of Data Analytics and Decision Support System in Crop Health Monitoring. Weather Risk Management Services (WRMS) [Internet]. 2023; Available from: https://timesofindia.indiatimes.com/blogs/voices/role-of-data-analytics-and-decision-support-system-in-crop-health-monitoring/?frmapp=yes&source=app,

[23] Singh H, Sharma N. Decision Support System for Precision Farming. Int J Comput Technol. 2013;4(1):76–81.