Optimizing Soybean Cultivation Efficiency through Agricultural Technology Integration in Plant Monitoring System
DOI:
https://doi.org/10.38035/gijes.v1i2.93Keywords:
Optimization, Efficiency, Monitoring, Agriculture, Soybean.Abstract
Agriculture plays a crucial role in fulfilling global food needs and promoting societal well-being. Soybean cultivation, as a strategic food crop, offers essential protein sources for humans and livestock while enhancing soil fertility through nitrogen fixation. However, the increasing global demand for soybeans poses challenges for farmers, particularly in terms of cultivation efficiency. These challenges are further exacerbated by climate change, land use, disease threats, and commodity price fluctuations. The advancement of agricultural technology, such as IoT, remote sensing, artificial intelligence, and predictive modeling, holds significant promise in improving soybean cultivation's efficiency and productivity. Precision agriculture emerges as a pivotal approach to support agricultural efficiency, productivity, and profitability. Expert systems and image processing techniques like artificial neural networks and genetic algorithms play a vital role in implementing precision agriculture. Information technology's use in precision agriculture focuses on data collection, analysis, and application in farming. Despite considerable research proposing technology integration in soybean cultivation, comprehensive studies on its potential integration remain limited. Thus, this international research aims to analyze the prospects of integrating agricultural technology into plant monitoring systems. Its primary goal is to contribute to the development of sustainable and efficient agricultural practices, considering environmental conditions and natural resource potentials. The findings will serve as a strategic foundation for agricultural stakeholders and policymakers to enhance soybean cultivation's sustainability, productivity, and quality, while effectively addressing global food challenges in the future.
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Ahmad, N., & Misra, S. (2019). Precision agriculture using Internet of Things (IoT): A review. International Journal of Computer Applications, 180(27), 1-7.
Akhter, M., Hasan, M. A., & Rahman, M. M. (2019). IoT-based smart irrigation and monitoring system for soybean cultivation. International Journal of Advanced Research in Computer Science, 10(2), 23-31.
Anderson, D., Walker, G., & Harris, T. (2022). Testing and Implementing IP-Camera Hardware and Software in Security Systems: A Practical Guide. International Journal of Computer Science and Applications, 17(3), 89-104.
Anwar, A., Hasan, M. M., & Shamsuddoha, M. (2017). Design and Fabrication of a Small Scale Soybean Harvester. International Journal of Engineering Sciences & Research Technology (IJESRT), 6(5), 210-215.
Bai, X., Xiao, Y., Liu, S., & Rosin, P. L. (2017). An integrated framework for weed segmentation in pepper fields for robotic precision farming. Computers and Electronics in Agriculture, 142, 205-216. DOI: 10.1016/j.compag.2017.09.022.
Baligar, V. C., Fageria, N. K., & He, Z. L. (2017). Nutrient use efficiency in plants. Springer.
Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and Internet of Things: a survey. Future Generation Computer Systems, 56, 684-700.
Buchanan, B. B., Gruissem, W., & Jones, R. L. (2015). Biochemistry & Molecular Biology of Plants (2nd ed.). American Society of Plant Biologists.
Carrión, G. L., Martín, Á. B., Suárez, J. P., & Castellanos, J. L. (2019). IoT-Based Monitoring and Control System for Precision Agriculture. Sensors, 19(15), 3386. doi:10.3390/s19153386.
Chen, H., Li, Q., & Wang, G. (2020). Adaptation Strategies for Soybean Cultivation under Extreme Climatic Conditions in High-Production Areas. Agriculture and Environment, 28(2), 201-215.
Chen, L., Wu, Q., & Zhou, S. (2019). Integrated Pest Management in Soybean Farming using IoT-based Monitoring Systems. Journal of Agricultural Engineering Research, 21(3), 320-335.
Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop/weed discrimination in robotic farming. Journal of Field Robotics, 35(8), 1248-1272.
Choudhury, D., Akter, R., Rahman, M., & Das, P. P. (2017). Design and implementation of an automatic irrigation system for soybean plants using AT89S8252 microcontroller. International Journal of Engineering and Applied Sciences, 4(9), 11-18.
Choudhury, S., & Sharma, S. (2019). Physiological and biochemical changes in soybean under different light conditions. Journal of Plant Interactions, 14(1), 342-352. DOI: 10.1080/17429145.2019.1602581.
Corbari, C., Fornasiero, A., Gottardi, S., Mezzalira, G., & Montesi, M. (2017). The Use of Unmanned Aerial Vehicles (UAV) and Machine Learning Techniques for Precision Agriculture: A Review. Precision Agriculture, 18(4), 653-683.
Cui, J., Qiu, J., & Huang, X. (2018). Design of Agricultural Pesticide Residue Detection System Based on Android. In 2018 3rd International Conference on System Reliability and Safety (ICSRS). IEEE.
Du, X., Yin, X., & Xu, X. (2022). Environmental sensing in smart agriculture: Recent developments and future perspectives. Biosystems Engineering, 217, 171-190. DOI: 10.1016/j.biosystemseng.2021.09.013
Duan, Y., Zhang, S., & Liu, J. (2020). Survey and Analysis of Environmental Data for Understanding Environmental Conditions and Natural Resource Potentials. International Journal of Environmental Research and Public Health, 17(4), 1350. DOI: 10.3390/ijerph17041350.
FAO. (2021). FAO Crops Statistics. Food and Agriculture Organization of the United Nations. http://www.fao.org/faostat/en/#data/QC.
Gao, Y., Zhang, L., & Zhang, Y. (2020). Integration of Artificial Intelligence and Remote Sensing for Land Preparation: A Review. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 11(3), 34-51.
Gao, Y., Zhu, S., Zhang, T., & Zhang, L. (2019). Real-time calibration of IP cameras using an adaptive brightest and contrast adjustment method. Journal of Visual Communication and Image Representation, 60, 42-52.
Garcia, R., Martinez, M., & Hernandez, P. (2019). "AI-Based Smart Irrigation System: A Review." In Proceedings of the 5th International Conference on Intelligent Sustainable Systems (ICISS 2020), pp. 651-657.
Garcia, R., Martinez, M., & Hernandez, P. (2019). [Judul artikel tidak disebutkan]. Jurnal Penelitian Pertanian, 25(3), 123-135.
Garcia, R., Martinez, M., & Hernandez, P. (2019). IoT-based Plant Monitoring System for Optimizing Soybean Cultivation Efficiency. Journal of Agricultural Science and Technology, 12(3), 225-238. Retrieved from: https://jast.modares.ac.ir/article-23-30872-en.html
Ghatak, S., Choudhury, T., & Chowdhury, D. (2020). AI-based Smart Agricultural System for Crops: A Review. International Journal of Advanced Science and Technology, 29(7), 5373-538
Gupta, R., & Singh, S. (2021). AI and Machine Learning-Based Prediction of Crop Prices: A Review. International Journal of Agricultural Economics, 10(3), 123-135.
Hameed, I. A., Hu, B., Tian, L., Xie, C., & Zhang, C. (2021). A review of plant growth monitoring technologies: Opportunities and challenges. Precision Agriculture, 22(4), 659-682. DOI: 10.1007/s11119-020-09724-1.
Hamilton, K. N., Kroschel, J., Saito, K., & Barros, E. (2019). [Title of the article is not provided]. Jurnal Pertanian Berkelanjutan, 42(4), 256-268.
Hanafy, M. S., & El-Din, A. S. (2018). Effect of some micro-elements on growth, yield, oil content, and protein fraction of soybean plant (Glycine max L.). Annals of Agricultural Sciences, 63(2), 165-170.
Harti, A. O. R., & Marina, I. (2022). Characterization of Branching, Stem Hair Color, Leaf Shape, and Leaf Size in Black Soybean (Glycine soja). Pro-STek, 4(2), 115-127.
Hussein, M. M., Baloch, M. J., Ali, S., Zhang, G., & Ibrahim, M. (2021). Drought Stress and Plant Health: Mechanisms, Implications, and Management Strategies. Plants, 10(2), 312. https://doi.org/10.3390/plants10020312.
Jain, V., & Soni, A. (2021). Precision Agriculture using Artificial Neural Networks: A Review. International Journal of Modern Computer Science and Engineering, 9(2), 28-34.
Jhanjhi, N. Z., Rajora, A., Manikonda, V., & Kumar, R. (2021). Weed detection and classification in soybean crops using machine learning. Computers and Electronics in Agriculture, 184, 106059.
Jin, X., Zhang, T., Lv, P., & Zhang, S. (2020). A Framework for Integrating Agricultural Technology into Plant Monitoring Systems. International Journal of Agricultural and Biological Engineering, 13(2), 71-82.
Kamal, A., & Madramootoo, C. A. (2019). Review of farm-level automation technologies and potential adoption by small-scale farmers. Agricultural Systems, 173, 198-210.
Khan, S., & Haider, S. I. (2018). Precision agriculture and its role in soybean cultivation: A review. International Journal of Agriculture and Biology, 20(6), 1259-1270.
Kumar, A., Pandey, P., & Singh, S. K. (2019). Precision Fertilization in Agriculture using IoT and Machine Learning. International Journal of Computer Applications, 182(28), 23-28.
Kumar, S., & Singh, P. (2019). Image Data Collection and Analysis Techniques for Precision Agriculture Monitoring. International Journal of Agricultural and Biological Engineering, 12(4), 31-43.
Lee, C., Kim, S., & Park, H. (2020). "IoT-based Plant Monitoring System for Optimizing Soybean Cultivation." International Journal of Agricultural Science, 30(2), 87-98.
Lee, J., Lee, S., & Kim, H. (2020). An integrated system for smart farming based on IoT and cloud computing. Computers and Electronics in Agriculture, 174, 105438.
Li, C., Zhang, S., Wang, Y., & Li, Z. (2020). Integrating Monitoring Technology for Sustainable Soybean Cultivation: A Case Study in China. International Journal of Agricultural Sustainability, 18(3), 267-281.
Li, Y., D. Liu, H. Wang, and J. Li. (2021). "Advances in Internet of Things and Big Data for Smart Agriculture: A Case Study in Soybean Cultivation." Frontiers in Plant Science, 12, 678423.
Li, Y., Zhang, L., Zhang, Y., & Cheng, C. (2020). An Intelligent Irrigation Control System Based on AI and Machine Learning. International Journal of Agricultural and Biological Engineering, 13(2), 77-88.
Miao, Y., Li, M., Qiao, Y., & Guo, J. (2021). Responses of Soybean Morphological and Yield Characteristics to Different Shading Treatments. Journal of Plant Growth Regulation, 40(1), 191-202.
Mishra, S., & Joshi, M. (2021). Advanced Technologies in Crop Monitoring and Precision Agriculture for Future Farming. Journal of Agricultural Science and Technology, 23(1), 1-20.
Misra, A., Saha, S., & Mishra, A. (2021). Real-time Monitoring of Environmental Parameters in Soybean Cultivation using IoT-based Sensor Network. International Journal of Agricultural Engineering, 10(3), 123-135.
Ort, D. R., Merchant, S. S., Alric, J., Barkan, A., Blankenship, R. E., Bock, R., ... & Long, S. P. (2015). Redesigning photosynthesis to sustainably meet global food and bioenergy demand. Proceedings of the National Academy of Sciences, 112(28), 8529-8536.
Rahma, A. O., & Marina, I. (2023). Comparison of growth and yield of soybean (Glycine max L) with variation of biofertilizer dosage in the rainy season. Pro-STek, 5(1), 36-43.
Rahman, M. M., & Biswas, D. K. (2021). An IoT-Based Smart Agriculture Monitoring and Decision Support System. Applied Sciences, 11(3), 1359.
Santos, C., Santos, L., & Torgo, L. (2021). "A data-driven irrigation recommendation system for precision agriculture." Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), 1-8.
Santos, R., Silva, M., & Oliveira, F. (2022). Integration of IoT and Big Data Analytics in Soybean Farming for Improved Crop Management. Precision Agriculture, 38(4), 412-425.
Sato, T., Watanabe, S., Yokota, T., & Tajima, S. (2021). Quantitative analysis of the relationship between light conditions and pod development in soybean plants. Photosynthesis Research, 147(2), 159-166.
Satriadi, H., & Mawardi, I. (2019). Land suitability analysis for rice cultivation using geographic information system and multi-criteria decision analysis in Barito Kuala, Indonesia. IOP Conference Series: Earth and Environmental Science, 314(1), 012003.
Shimelis, H. A., & Mohamed, Y. A. (2019). [Title of the article not mentioned]. Jurnal Perlindungan Tanaman Tropis, 36(2), 87-98.
Smith, J., & Brown, A. (2018). Time series analysis for forecasting agricultural commodity prices. Journal of Agricultural Economics, 45(3), 345-358.
Smith, J., & Brown, A. (2021). "Advancing Agricultural Technology Integration: A Review of Monitoring Systems for Crop Management." Journal of Agricultural Engineering, 45(3), 156-172.
Song, J., Kim, J., Lee, S., & Park, S. (2021). Developing a Framework for Precision Agriculture: Integrating Agricultural Technology into Plant Monitoring Systems. Biosystems Engineering, 204, 28-39.
Sulieman, S., Tran, L. S. P., & Atlin, G. N. (2019). Legume Nitrogen Fixation in Soils with Nitrogen Fertilizer Application: A Review. Agronomy, 9(4), 188. DOI: 10.3390/agronomy9040188
Tayyab, M., Mahmood, H., Ahmed, R., & Hanif, U. (2020). Land suitability analysis for wheat crop using GIS and multi-criteria evaluation: A case study of Okara, Pakistan. Arabian Journal of Geosciences, 13(2), 1-12.
Torrico, D. D., Funes-Monzote, F. R., & Vanegas, R. (2016). The impact of land use change on soil physical properties in the tropical Andes. Agriculture, Ecosystems & Environment, 231, 178-186.
Truong, H. K., Baek, S. Y., & Kim, K. S. (2019). Integrating IoT and AI for precision soybean farming: A case study. Journal of Information Processing Systems, 15(4), 838-850.
Uraz, S., Genc, M. (2020). A Study on Harvesting Losses of Soybean Threshing Process in Different Crop Densities. Journal of Agricultural Sciences, 26(1), 131-138.
Wang, C., Zhu, H., Zhang, Y., & Song, S. (2018). A UAV-based crop monitoring system for precision agriculture. Sensors, 18(9), 2837. DOI: 10.3390/s18092837.
Wang, D., & Shannon, D. K. (2019). Precision Agriculture Technologies and Their Economic Impact on Farms in the United States. Journal of Agricultural and Resource Economics, 44(1), 109-128. DOI: 10.22004/ag.econ.281904.
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Published
2023-09-04
How to Cite
Marina, I. ., Sujadi, H. ., & Rakhmi Indriana, K. . (2023). Optimizing Soybean Cultivation Efficiency through Agricultural Technology Integration in Plant Monitoring System. Greenation International Journal of Engineering Science, 1(2), 115–127. https://doi.org/10.38035/gijes.v1i2.93
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