Redesigning the Future of Farmland Management: The Precision FarmTrac Framework
Keywords:
Farmer Management, Farmland Mapping, Framework, Precision Agriculture, Image Processing, Rice CropAbstract
Farmland management in the agricultural sector involves a number of planning and decision-making responsibilities, such as providing farmers with information, selecting crops, and maintaining farmers' land. Agriculture holds significant importance in the development and progress of a country. Challenges in the agriculture sector constantly limit a country's progress. The answer to addressing these difficulties is to improve traditional farmland management and adopt precision agriculture approaches. To address this challenge, a precision agriculture framework for farmland management is proposed. The Precision FarmTrac framework was designed based on interviews with the Department of Agriculture office, as well as data provided from document analysis and observations. The proposed framework shall assist the Department of Agriculture in monitoring farmers' farmland management. The proposed framework shall be well-suited to effectively oversee and monitor farmers' farmland, enhancing traditional land management practices, monitoring crop growth, and ensuring equitable distribution of benefits among farmers and stakeholders. As a result, the Precision FarmTrac framework becomes an essential tool for transforming the future of agriculture by increasing productivity, and maintaining the sustainable development of farmland resources. This framework can be used by the department of agriculture to facilitate the dissemination of information to various agricultural sectors and farmers, allowing for more efficient and effective farmer’s and farmland monitoring.
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A. Joshi, B. Pradhan, S. Gite, and S. Chakraborty, “Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review,” Remote Sens., vol. 15, no. 8, 2023, doi: 10.3390/rs15082014.
A. Yazdinejad et al., “applied sciences A Review on Security of Smart Farming and Precision Agriculture : Security Aspects , Attacks , Threats and Countermeasures,” 2021.
C. Verdouw, B. Tekinerdogan, A. Beulens, and S. Wolfert, “Digital twins in smart farming,” Agric. Syst., vol. 189, no. December 2020, p. 103046, 2021, doi: 10.1016/j.agsy.2020.103046.
K. G. Arvanitis and E. G. Symeonaki, “Agriculture 4.0: The Role of Innovative Smart Technologies Towards Sustainable Farm Management,” Open Agric. J., vol. 14, no. 1, pp. 130–135, 2020, doi: 10.2174/1874331502014010130.
I. Cisternas, I. Velásquez, A. Caro, and A. Rodríguez, “Systematic literature review of implementations of precision agriculture,” Comput. Electron. Agric., vol. 176, no. May, p. 105626, 2020, doi: 10.1016/j.compag.2020.105626.
R. K. Singh and G. S. Member, “AgriFusion : An Architecture for IoT and Emerging Technologies Based on a Precision Agriculture Survey,” pp. 136253–136283, 2021.
R. Alfred, J. O. E. H. Obit, and C. P. Chin, “Towards Paddy Rice Smart Farming : A Review on Big Data , Machine Learning , and Rice Production Tasks,” vol. 9, 2021, doi: 10.1109/ACCESS.2021.3069449.
E. Said Mohamed, A. A. Belal, S. Kotb Abd-Elmabod, M. A. El-Shirbeny, A. Gad, and M. B. Zahran, “Smart farming for improving agricultural management,” Egypt. J. Remote Sens. Sp. Sci., vol. 24, no. 3, pp. 971–981, 2021, doi: 10.1016/j.ejrs.2021.08.007.
A. Ravi Kumar, L. B. Yadav, J. B. S K, and P. Sudha, “Precision Agriculture: a Review on Its Techniques and Technologies,” Int. Res. J. Mod. Eng. Technol. Sci. @International Res. J. Mod. Eng., no. 09, pp. 2582–5208, 2020, [Online]. Available: www.irjmets.com
S. R. Nandurkar, V. R. Thool, and R. C. Thool, “Design and development of precision agriculture system using wireless sensor network,” 1st Int. Conf. Autom. Control. Energy Syst. - 2014, ACES 2014, 2014, doi: 10.1109/ACES.2014.6808017.
I. V. Kovalev and N. A. Testoyedov, “Modern unmanned aerial technologies for the development of agribusiness and precision farming,” IOP Conf. Ser. Earth Environ. Sci., vol. 548, no. 5, 2020, doi: 10.1088/1755-1315/548/5/052080.
P. K. Kashyap, S. Kumar, S. M. Ieee, A. Jaiswal, and M. Prasad, “Towards Precision Agriculture : IoT - enabled Intelligent Irrigation Systems Using Deep Learning Neural Network,” vol. XX, no. XX, pp. 1–11, 2021, doi: 10.1109/JSEN.2021.3069266.
T. T. Nguyen et al., “Monitoring agriculture areas with satellite images and deep learning,” Appl. Soft Comput. J., vol. 95, p. 106565, 2020, doi: 10.1016/j.asoc.2020.106565.
Q. Yuan, H. Shen, T. Li, Z. Li, S. Li, and Y. Jiang, “Remote Sensing of Environment Deep learning in environmental remote sensing : Achievements and challenges,” Remote Sens. Environ., vol. 241, no. January, p. 111716, 2020, doi: 10.1016/j.rse.2020.111716.
A. Shafique, G. Cao, Z. Khan, and M. Asad, “Deep Learning-Based Change Detection in Remote Sensing Images : A Review,” pp. 1–40, 2022.
L. Chen, S. Li, Q. Bai, J. Yang, S. Jiang, and Y. Miao, “Review of Image Classification Algorithms Based on Convolutional Neural Networks,” pp. 1–51, 2021.
A. Gafurov, “Automated Mapping of Cropland Boundaries Using Deep Neural Networks,” AgriEngineering, vol. 5, no. 3, pp. 1568–1580, 2023, doi: 10.3390/agriengineering5030097.
J. J. S. Mercado, E. V. Lansangan, E. G. Baltazar, A. C. Lagasca, and E. M. Valiente, “Assessment of Key Players in the Special Rice Value Chain in Nueva Ecija, Philippines,” Open J. Ecol., vol. 13, no. 06, pp. 422–433, 2023, doi: 10.4236/oje.2023.136026.
L. Prado et al., “International Journal of Applied Earth Observations and Geoinformation A review on deep learning in UAV remote sensing,” vol. 102, 2021, doi: 10.1016/j.jag.2021.102456.
Y. Tang, S. Dananjayan, C. Hou, Q. Guo, S. Luo, and Y. He, “A survey on the 5G network and its impact on agriculture: Challenges and opportunities,” Comput. Electron. Agric., vol. 180, no. September 2020, p. 105895, 2021, doi: 10.1016/j.compag.2020.105895.
N. Zaabar, S. Niculescu, and M. M. Kamel, “Application of Convolutional Neural Networks With Object-Based Image Analysis for Land Cover and Land Use Mapping in Coastal Areas: A Case Study in Ain Témouchent, Algeria,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, pp. 5177–5189, 2022, doi: 10.1109/JSTARS.2022.3185185.
A. C. Tolentino and T. D. Palaoag, “Precision agriculture : exploration of deep learning models for farmland mapping,” vol. 34, no. 1, pp. 592–601, 2024, doi: 10.11591/ijeecs.v34.i1.pp592-601.
J. Munz, N. Gindele, and R. Doluschitz, “Exploring the characteristics and utilisation of Farm Management Information Systems ( FMIS ) in Germany,” Comput. Electron. Agric., vol. 170, no. January, p. 105246, 2020, doi: 10.1016/j.compag.2020.105246.
A. T. Balafoutis, F. K. V. Evert, and S. Fountas, “Smart Farming Technology Trends : Economic and,” Agronomy, vol. 10, p. 743, 2020.
M. Javaid, A. Haleem, R. P. Singh, and R. Suman, “Enhancing smart farming through the applications of Agriculture 4.0 technologies,” Int. J. Intell. Networks, vol. 3, no. August, pp. 150–164, 2022, doi: 10.1016/j.ijin.2022.09.004.
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