Enhancing Agricultural Infrastructure Development through Remote Sensing using Machine Learning
DOI:
https://doi.org/10.32628/IJSRCEKeywords:
Soil Moisture, Temperature, Water Accessibility, Remote Sensing, Machine Learning, Pest Management, Agricultural InfrastructureAbstract
Agriculture is one of the factors that contribute to the economic development of most developing countries but for it to be successful, it needs some attributes like soil moisture, temperature, water accessibility, and pest management. Sustainable agriculture depends on the efficient surveillance and categorization of crops, an essential action for its promotion. Remote sensing and machine learning are viable methods for fulfilling these tasks. This paper focuses on an examination of how these elements can be adopted in civil engineering to enhance agricultural infrastructure. Through remote sensing and machine learning technologies in civil engineering, crop conditions are precisely observed and resource management is optimized more effectively. Through satellite imagery and environmental data analysis, engineers are able to monitor crop health, identify water stress, and optimize irrigation operations. For example, classification algorithms are useful in distinguishing the different types of crops and discovering pest outbreaks, which allow preventive treatments to be put in place. The issue of water scarcity has serious consequences in the agricultural sector. Through the use of remote sensing data with machine learning algorithms, engineers can locate exhaust regions and apply irrigation techniques with a high level of precision. In addition, accurate classification of crops helps in maximizing the utilization of land and resources as well as their allocation. Remote sensing and machine learning application contribute to more effective pest and disease management. Engineers through environmental data analytics can detect early signs of infestation and forecast epidemics because of which they can make timely interventions and reduce crop destructions. By a combination of remote sensing and machine learning in civil engineering, the prospects of increasing the quality of agricultural infrastructure have a novelty. Enabling interdisciplinary collaboration among engineers ensures that the engineers can design unique solutions to the complex problems that agriculture is faced with. This not only gives the agriculture a long-term stability, but also it is a very important factor that stimulates economic development in the countries which are developing.
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A. E. Engidaw, “Small businesses and their challenges during COVID-19 pandemic in developing countries: in the case of Ethiopia,” J. Innov. Entrep., vol. 11, no. 1, p. 1, 2022.
Y. Lupenko, O. Khodakivska, O. Nechyporenko, and O. Shpykuliak, “The state and trends of agricultural development in the structure of the national economy of Ukraine,” 2022.
M. Pu and Y. Zhong, “Rising concerns over agricultural production as COVID-19 spreads: Lessons from China,” Glob. Food Sec., vol. 26, p. 100409, 2020.
P. Prus and M. Sikora, “The impact of transport infrastructure on the sustainable development of the region—Case study,” Agriculture, vol. 11, no. 4, p. 279, 2021.
I. Ghafoor et al., “Slow-release nitrogen fertilizers enhance growth, yield, NUE in wheat crop and reduce nitrogen losses under an arid environment,” Environ. Sci. Pollut. Res., vol. 28, no. 32, pp. 43528–43543, 2021.
C. Jamroen, P. Komkum, C. Fongkerd, and W. Krongpha, “An intelligent irrigation scheduling system using low-cost wireless sensor network toward sustainable and precision agriculture,” IEEE Access, vol. 8, pp. 172756–172769, 2020.
R. Shukla et al., “Detecting crop health using machine learning techniques in smart agriculture system,” J. Sci. Ind. Res., vol. 80, no. 08, pp. 699–706, 2021.
th International Symposium on Methodologies for Intelligent Systems, ISMIS 2020, vol. 12117 LNAI. 2020.
T. Shmatkovska, A. Nikolaeva, M. Zabedyuk, Y. Sheiko, and Y. Grudzevych, “Increasing the efficiency of the labour resources usage of agrosector enterprises in the system of sustainable development of the rural territories: a case study of Ukraine.,” 2020.
S. K. Lowder, M. V Sánchez, and R. Bertini, “Which farms feed the world and has farmland become more concentrated?,” World Dev., vol. 142, p. 105455, 2021.
K. A. Wyckhuys, M. D. Day, M. J. Furlong, M. S. Hoddle, A. Sivapragasam, and H. D. Tran, “Biological control successes and failures: Indo-Pacific/Oriental region,” Biol Control Glob Impacts Chall Futur. Dir Pest Manag, pp. 438–466, 2021.
V. Sharma, A. K. Tripathi, and H. Mittal, “Technological revolutions in smart farming: Current trends, challenges & future directions,” Comput. Electron. Agric., vol. 201, p. 107217, 2022.
S. Khanal, K. Kc, J. P. Fulton, S. Shearer, and E. Ozkan, “Remote sensing in agriculture—accomplishments, limitations, and opportunities,” Remote Sens., vol. 12, no. 22, p. 3783, 2020.
Y. Li et al., “Identification of weeds based on hyperspectral imaging and machine learning,” Front. Plant Sci., vol. 11, p. 611622, 2021.
A. Carballo et al., “LIBRE: The multiple 3D LiDAR dataset,” in 2020 IEEE intelligent vehicles symposium (IV), 2020, pp. 1094–1101.
I. H. Sarker, “Machine learning: Algorithms, real-world applications and research directions,” SN Comput. Sci., vol. 2, no. 3, p. 160, 2021.
K. John, I. Abraham Isong, N. Michael Kebonye, E. Okon Ayito, P. Chapman Agyeman, and S. Marcus Afu, “Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil,” Land, vol. 9, no. 12, p. 487, 2020.
S. Taghvaeian et al., “Irrigation scheduling for agriculture in the United States: The progress made and the path forward,” Trans. ASABE, vol. 63, no. 5, pp. 1603–1618, 2020.
E. Karunathilake, A. T. Le, S. Heo, Y. S. Chung, and S. Mansoor, “The path to smart farming: Innovations and opportunities in precision agriculture,” Agriculture, vol. 13, no. 8, p. 1593, 2023.
L. Pereira Ribeiro Teodoro et al., “Eucalyptus Species Discrimination Using Hyperspectral Sensor Data and Machine Learning,” Forests, vol. 15, no. 1, p. 39, 2023.
J. Ngondo, J. Mango, R. Liu, J. Nobert, A. Dubi, and H. Cheng, “Land-use and land-cover (LULC) change detection and the implications for coastal water resource management in the Wami--Ruvu Basin, Tanzania,” Sustainability, vol. 13, no. 8, p. 4092, 2021.
A. Cravero, S. Pardo, S. Sepúlveda, and L. Muñoz, “Challenges to use machine learning in agricultural big data: a systematic literature review,” Agronomy, vol. 12, no. 3, p. 748, 2022.
U. Chatterjee and S. Majumdar, “Impact of land use change and rapid urbanization on urban heat island in Kolkata city: A remote sensing based perspective,” J. urban Manag., vol. 11, no. 1, pp. 59–71, 2022.
K. Wilder-Davis et al., “From fast food to a well-balanced diet: toward a programme focused approach to feedback,” Pract. Res. High. Educ., vol. 14, no. 1, pp. 3–15, 2021.
R. Sharma, S. S. Kamble, A. Gunasekaran, V. Kumar, and A. Kumar, “A systematic literature review on machine learning applications for sustainable agriculture supply chain performance,” Comput. Oper. Res., vol. 119, p. 104926, 2020.
R. Thakur and P. Panse, “Classification performance of land use from multispectral remote sensing images using decision tree, K-nearest neighbor, random forest and support vector machine using EuroSAT data,” Int. J. Intell. Syst. Appl. Eng., vol. 10, no. 1s, pp. 67–77, 2022.
B. Efron, “Prediction, estimation, and attribution,” Int. Stat. Rev., vol. 88, pp. S28--S59, 2020.
B. M. de Silva, K. Champion, M. Quade, J.-C. Loiseau, J. N. Kutz, and S. L. Brunton, “Pysindy: a python package for the sparse identification of nonlinear dynamics from data,” arXiv Prepr. arXiv2004.08424, 2020.
M. Weiss, F. Jacob, and G. Duveiller, “Remote sensing for agricultural applications: A meta-review,” Remote Sens. Environ., vol. 236, p. 111402, 2020.
M. Etemadi and M. Hajizadeh, “User fee removal for the poor: a qualitative study to explore policies for social health assistance in Iran,” BMC Health Serv. Res., vol. 22, no. 1, p. 250, 2022.
G. Meraj et al., “Assessing the yield of wheat using satellite remote sensing-based machine learning algorithms and simulation modeling,” Remote Sens., vol. 14, no. 13, p. 3005, 2022.
K. Y. Lee, L. Sheehan, K. Lee, and Y. Chang, “The continuation and recommendation intention of artificial intelligence-based voice assistant systems (AIVAS): the influence of personal traits,” Internet Res., vol. 31, no. 5, pp. 1899–1939, 2021.
D. Shete and P. Khobragade, “An empirical analysis of different data visualization techniques from statistical perspective,” in AIP Conference Proceedings, 2023.
S. Saeed, “A customer-centric view of E-commerce security and privacy,” Appl. Sci., vol. 13, no. 2, p. 1020, 2023.
J. P. Aryal, T. B. Sapkota, R. Khurana, A. Khatri-Chhetri, D. B. Rahut, and M. L. Jat, “Climate change and agriculture in South Asia: Adaptation options in smallholder production systems,” Environ. Dev. Sustain., vol. 22, no. 6, pp. 5045–5075, 2020.
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