Progress and Potential Drawbacks of Modern Agricultural Technologies: A Literature Review
DOI:
https://doi.org/10.24925/turjaf.v12i10.1858-1864.6834Anahtar Kelimeler:
Agricultural technology- progress- farm automation- smart agriculture- Modern agricultureÖzet
The production of agriculture has undergone new modifications as a result of agricultural technologies. These not only boost agricultural output but may also significantly raise the caliber of produced food, cut labor expenses, boost farmers' incomes, and achieve agricultural modernization. The use of precision agriculture (PA) is expanding due to the rapid socioeconomic changes that are occurring in certain developing nations. There are enormous ramifications for urbanization, energy consumption, and economic growth in certain developing nations when fundamental changes occur. The research status and current agricultural technology achievements are carefully summarized in this study. In-depth discussions of thirteen significant agricultural technologies are provided in this article. All significant technologies from developed countries are discussed so that under-developed and lower-developed countries will benefit from this paper. Finally, some fresh concepts for each technology are offered, and potential issues in establishing such sophisticated technologies are identified. The main objective of this review is to increase knowledge of modern agriculture and the development process in the agricultural field.
Referanslar
Atherton, H. R., & Li, P. (2023). Hydroponic Cultivation of Medicinal Plants—Plant Organs and Hydroponic Systems: Techniques and Trends. Horticulturae, 9(3), 349.
Bhat, S. A., Huang, N.-F., Sofi, I. B., & Sultan, M. (2021). Agriculture-food supply chain management based on blockchain and IoT: A narrative on enterprise blockchain interoperability. Agriculture, 12(1), 40.
Brown, R. (2017). Laser Scarecrows: Gimmick or Solution?
Brown, R. N., & Brown, D. H. (2021). Robotic laser scarecrows: A tool for controlling bird damage in sweet corn. Crop Protection, 146, 105652.
Bwambale, E., Abagale, F. K., & Anornu, G. K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management, 260, 107324.
Chen, J., Wang, J., Wang, D., Zhao, W., Chang, H., & Feng, S. (2022). Instantaneous Centimeter-Level PPP-RTK Positioning Technology Based on Low-Cost Mass Market GNSS Devices. 384–392.
Chew, K.-M., Tan, S. C.-W., Loh, G. C.-W., Bundan, N., & Yiiong, S.-P. (2020). IoT soil moisture monitoring and irrigation system development. 247–252.
Cisternas, I., Velásquez, I., Caro, A., & Rodríguez, A. (2020). Systematic literature review of implementations of precision agriculture. Computers and Electronics in Agriculture, 176, 105626.
Demestichas, K., Peppes, N., Alexakis, T., & Adamopoulou, E. (2020). Blockchain in agriculture traceability systems: A review. Applied Sciences, 10(12), 4113.
Dimitrijević, M. S. (2023). Technological progress in the function of productivity and sustainability of agriculture: The case of innovative countries and the Republic of Serbia. Journal of Agriculture and Food Research, 14, 100856.
Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4), 4377–4383.
El Bilali, H., Bassole, I. H. N., Dambo, L., & Berjan, S. (2020). Climate change and food security. Agriculture & Forestry/Poljoprivreda i Sumarstvo, 66(3).
Eyhorn, F., Muller, A., Reganold, J. P., Frison, E., Herren, H. R., Luttikholt, L., Mueller, A., Sanders, J., Scialabba, N. E.-H., & Seufert, V. (2019). Sustainability in global agriculture driven by organic farming. Nature Sustainability, 2(4), 253–255.
Farooq, M. S., Riaz, S., Rehman, I. U., Khan, M. A., & Hassan, B. (2023). A Blockchain-Based Framework to Make the Rice Crop Supply Chain Transparent and Reliable in Agriculture. Systems, 11(9), 476.
Freeland, R., Allred, B., Eash, N., Martinez, L., & Wishart, D. (2019). Agricultural drainage tile surveying using an unmanned aircraft vehicle paired with Real-Time Kinematic positioning—A case study. Computers and Electronics in Agriculture, 165, 104946.
Fukase, E., & Martin, W. (2020). Economic growth, convergence, and world food demand and supply. World Development, 132, 104954.
Hung, L., Liu, C., Woldum, E., Au-Yeung, A., Berndt, A., Wallsworth, C., Horne, N., Gregorio, M., Mann, J., & Chaudhury, H. (2019). The benefits of and barriers to using a social robot PARO in care settings: A scoping review. BMC Geriatrics, 19, 1–10.
Islam, R., Parvin, A., Billah, M., Islam, M., Imran, M., Sarker, R., Uddin, M., Alam, M., & Abedin, M. (2020). Assessment of the effects of genetically modified (GM) foods: A brief study on health and environmental concerns. J. Mater. Environ. Sci, 11(10), 1676–1688.
Jaiswal, H., Singuluri, R., & Sampson, S. A. (2019). IoT and machine learning based approach for fully automated greenhouse. 1–6.
Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15–30.
Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1–12.
Jhariya, M. K., Banerjee, A., Meena, R. S., Kumar, S., & Raj, A. (2021). Sustainable intensification for agroecosystem services and management: An overview. Sustainable Intensification for Agroecosystem Services and Management, 1–35.
Ji, J., Chao, N., & Ding, J. (2019). Rumormongering of genetically modified (GM) food on Chinese social network. Telematics and Informatics, 37, 1–12.
Khan, N., Ray, R. L., Sargani, G. R., Ihtisham, M., Khayyam, M., & Ismail, S. (2021). Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture. Sustainability, 13(9), 4883.
Krasnikova, E., Karmeeva, Y., Aledo, M., Krasnikov, A., & Kalganov, S. (2019). Hemato-biochemical status of laboratory mice with a GM corn based diet. 315(4), 042005.
Kumar, K. A., & Rajitha, G. (2019). Alternate wetting and drying (AWD) irrigation-a smart water saving technology for rice: A review. International Journal of Current Microbiology and Applied Sciences, 8(3), 2561–2571.
Langner, T., Harant, A., Gomez-Luciano, L. B., Shrestha, R. K., Malmgren, A., Latorre, S. M., Burbano, H. A., Win, J., & Kamoun, S. (2021). Genomic rearrangements generate hypervariable mini-chromosomes in host-specific isolates of the blast fungus. PLoS Genetics, 17(2), e1009386.
Liliane, T. N., & Charles, M. S. (2020). Factors affecting yield of crops. Agronomy-climate change & food security, 9.
Mandal, A., Sarkar, B., Owens, G., Thakur, J., Manna, M., Niazi, N. K., Jayaraman, S., & Patra, A. K. (2020). Impact of genetically modified crops on rhizosphere microorganisms and processes: A review focusing on Bt cotton. Applied Soil Ecology, 148, 103492.
Manida, M., & Ganeshan, M. (2021). New Agriculture Technology in Modern Farming. Proceedings of the 2nd International Multidisciplinary Conference on Information Science, Management Research and Social Science. Chennai, Tamil nadu.
Mattivi, P., Pappalardo, S. E., Nikolić, N., Mandolesi, L., Persichetti, A., De Marchi, M., & Masin, R. (2021). Can commercial low-cost drones and open-source GIS technologies be suitable for semi-automatic weed mapping for smart farming? A case study in NE Italy. Remote Sensing, 13(10), 1869.
Mattson, N., & Lieth, J. H. (2019). Liquid culture hydroponic system operation. In Soilless Culture (pp. 567–585). Elsevier.
Mbuli, C. S., Fonjong, L. N., & Fletcher, A. J. (2021). Climate change and small farmers’ vulnerability to food insecurity in Cameroon. Sustainability, 13(3), 1523.
Nawaz, M. A., Golokhvast, K. S., Tsatsakis, A. M., Lam, H.-M., & Chung, G. (2020). GMOs, biodiversity and ecosystem processes. GMOs: Implications for Biodiversity Conservation and Ecological Processes, 3–17.
Nguyen, T. T., Hoang, T. D., Pham, M. T., Vu, T. T., Nguyen, T. H., Huynh, Q.-T., & Jo, J. (2020). Monitoring agriculture areas with satellite images and deep learning. Applied Soft Computing, 95, 106565.
Pradhananga, P., ElZomor, M., & Santi Kasabdji, G. (2021). Identifying the challenges to adopting robotics in the US construction industry. Journal of Construction Engineering and Management, 147(5), 05021003.
Pyingkodi, M., Thenmozhi, K., Nanthini, K., Karthikeyan, M., Palarimath, S., Erajavignesh, V., & Kumar, G. B. A. (2022). Sensor based smart agriculture with IoT technologies: A review. 1–7.
Radočaj, D., Jurišić, M., & Gašparović, M. (2022). The role of remote sensing data and methods in a modern approach to fertilization in precision agriculture. Remote Sensing, 14(3), 778.
Rameshkumar, D., Jagathjothi, N., Easwari, S., Rajesh, R., Muthuselvi, R., Kumar, P. N., Krishnakumare, B., Minithra, R., & Suresh, R. (2020). Vertical Farming-Agriculture of the Future. Indian Farmer, 7(11), 1013–1017.
Reddy, P., Kurnia, S., & Tortorella, G. L. (2022). Digital Food Supply Chain Traceability Framework. 82(1), 9.
Rehman, A., Saba, T., Kashif, M., Fati, S. M., Bahaj, S. A., & Chaudhry, H. (2022). A revisit of internet of things technologies for monitoring and control strategies in smart agriculture. Agronomy, 12(1), 127.
Rejeb, A., Abdollahi, A., Rejeb, K., & Treiblmaier, H. (2022). Drones in agriculture: A review and bibliometric analysis. Computers and Electronics in Agriculture, 198, 107017.
Rose, D. C., Lyon, J., de Boon, A., Hanheide, M., & Pearson, S. (2021). Responsible development of autonomous robotics in agriculture. Nature Food, 2(5), 306–309.
Saha, H. N., Chakraborty, S., & Roy, R. (2022). Integration of RFID and sensors in agriculture using IOT. In AI, Edge and IoT-based Smart Agriculture (pp. 361–372). Elsevier.
Sajja, G. S., Rane, K. P., Phasinam, K., Kassanuk, T., Okoronkwo, E., & Prabhu, P. (2023). Towards applicability of blockchain in agriculture sector. Materials Today: Proceedings, 80, 3705–3708.
Sharma, R. (2021). Artificial intelligence in agriculture: A review. 937–942.
Shrivastava, A., Nayak, C. K., Dilip, R., Samal, S. R., Rout, S., & Ashfaque, S. M. (2023). Automatic robotic system design and development for vertical hydroponic farming using IoT and big data analysis. Materials Today: Proceedings, 80, 3546–3553.
Suresh, S., & Chakaravarthi, G. (2022). RFID technology and its diverse applications: A brief exposition with a proposed Machine Learning approach. Measurement, 195, 111197.
Sylvester, G. (2019). E-agriculture in action: Blockchain for agriculture, opportunities and challenges. FAO.
Teklu, A., Simane, B., & Bezabih, M. (2023). Multiple adoption of climate-smart agriculture innovation for agricultural sustainability: Empirical evidence from the Upper Blue Nile Highlands of Ethiopia. Climate Risk Management, 39, 100477.
Tian, H., Wang, T., Liu, Y., Qiao, X., & Li, Y. (2020). Computer vision technology in agricultural automation—A review. Information Processing in Agriculture, 7(1), 1–19.
Wang, T., Chen, B., Zhang, Z., Li, H., & Zhang, M. (2022). Applications of machine vision in agricultural robot navigation: A review. Computers and Electronics in Agriculture, 198, 107085.
Wang, X. (2022). Managing land carrying capacity: Key to achieving sustainable production systems for food security. Land, 11(4), 484.
Xie, D., Chen, L., Liu, L., Chen, L., & Wang, H. (2022). Actuators and sensors for application in agricultural robots: A review. Machines, 10(10), 913.
Yan, X., & Li, J. (2019). Animal Intelligent Logistics Management Based on RFID Technology. Revista Científica de La Facultad de Ciencias Veterinarias, 29(6), 1772–1781.
Zhang, C., Marzougui, A., & Sankaran, S. (2020). High-resolution satellite imagery applications in crop phenotyping: An overview. Computers and Electronics in Agriculture, 175, 105584.
İndir
Yayınlanmış
Nasıl Atıf Yapılır
Sayı
Bölüm
Lisans
Bu çalışma Creative Commons Attribution-NonCommercial 4.0 International License ile lisanslanmıştır.
