Harnessing Disruptive Technologies for Agricultural Revolution: A Systematic Literature Review on Impact and Sustainability
Abstract
This literature review employs the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) framework to comprehensively analyze the impact of disruptive technologies on the agricultural sector, often characterized as the Agriculture Revolution. The review's primary focus lies on understanding how disruptive technologies influence business model transformation, enhance operational efficiency, promote sustainability, and mitigate supply chain disruptions within the agricultural domain. By systematically reviewing existing research, this study seeks to consolidate knowledge, identify research gaps, and provide valuable insights for the future development of agriculture in the era of disruptive technologies.
References
Aamer, A., Eka Yani, L., & Alan Priyatna, I. (2020). Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Operations and Supply Chain Management: An International Journal, 14(1), 1-13.
Abd El Kareem Gomaa, H. (2022). Modern Trends in the Development of Smart Agriculture Projects. International Journal of Modern Agriculture and Environment, 2(1), 33-44.
Altarturi, H. H., Nor, A. R. M., Jaafar, N. I., & Anuar, N. B. (2023). A bibliometric and content analysis of technological advancement applications in agricultural e-commerce. Electronic Commerce Research, 1-44.
Anitha, J., & Saranya, N. (2022). Cassava leaf disease identification and detection using deep learning approach. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 17(2).
Beltrán-Lugo, L., Izaguirre-Díaz de León, F., Peinado-Guevara, V., Peinado-Guevara, H., Herrera-Barrientos, J., Cuadras-Berrelleza, A. A., & Montoya-Leyva, M. Á. (2023). Sustainable Innovation Management in the Shrimp Sector of the Municipality of Guasave, State of Sinaloa, Mexico. Sustainability, 15(4), 3161.
Chandan, A., John, M., & Potdar, V. (2023). Achieving UN SDGs in Food Supply Chain Using Blockchain Technology. Sustainability, 15(3), 2109.
Chhetri, T. R., Hohenegger, A., Fensel, A., Kasali, M. A., & Adekunle, A. A. (2023). Towards improving prediction accuracy and user-level explainability using deep learning and knowledge graphs: A study on cassava disease. Expert Systems with Applications, 233, 120955.
Della Pelle, F., Bukhari, Q. U. A., Diduk, R. A., Scroccarello, A., Compagnone, D., & Merkoçi, A. (2023). Freestanding laser-induced two dimensional heterostructures for self-contained paper-based sensors. Nanoscale, 15(15), 7164-7175.
Enescu, F. M., Bizon, N., Onu, A., Răboacă, M. S., Thounthong, P., Mazare, A. G., & Șerban, G. (2020). Implementing blockchain technology in irrigation systems that integrate photovoltaic energy generation systems. Sustainability, 12(4), 1540.
Fennimore, S. A., & Cutulle, M. (2019). Robotic weeders can improve weed control options for specialty crops. Pest management science, 75(7), 1767-1774.
Gomes, J., Esteves, I., Neto, V. V. G., David, J. M. N., Braga, R., Arbex, W., Kassab, M., & de Oliveira, R. F. (2023). A scientific software ecosystem architecture for the livestock domain. Information and Software Technology, 160, 107240.
Henchion, M. M., Regan, Á., Beecher, M., & MackenWalsh, Á. (2022). Developing ‘Smart’Dairy Farming Responsive to Farmers and Consumer-Citizens: A Review. Animals, 12(3), 360.
Jansing, J., Schiermeyer, A., Schillberg, S., Fischer, R., & Bortesi, L. (2019). Genome editing in agriculture: technical and practical considerations. International journal of molecular sciences, 20(12), 2888.
Jung, J., Maeda, M., Chang, A., Bhandari, M., Ashapure, A., & Landivar-Bowles, J. (2021). The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in Biotechnology, 70, 15-22.
Kartika, B., Parson, S. W., Kassim, Z., & Chowdhury, A. J. K. (2022). Overview of halal freshwater aquaculture system: Malaysian perspectives. Water Conservation & Management, 6(1), 01-05.
Kaur, H. (2021). Modelling internet of things driven sustainable food security system. Benchmarking: An International Journal, 28(5), 1740-1760.
Kour, V. P., & Arora, S. (2020). Recent developments of the internet of things in agriculture: a survey. IEEE Access, 8, 129924-129957.
Lachman, J., & López, A. (2022). The nurturing role of the local support ecosystem in the development of the Agtech sector in Argentina. Journal of Agribusiness in Developing and Emerging Economies, 12(4), 714-729.
Mahmud, M. S. A., Abidin, M. S. Z., Emmanuel, A. A., & Hasan, H. S. (2020). Robotics and automation in agriculture: present and future applications. Applications of Modelling and Simulation, 4, 130-140.
Masud, M. M., Azam, M. N., Mohiuddin, M., Banna, H., Akhtar, R., Alam, A. F., & Begum, H. (2017). Adaptation barriers and strategies towards climate change: Challenges in the agricultural sector. Journal of Cleaner Production, 156, 698-706.
Mohamed, E. S., Belal, A., Abd-Elmabod, S. K., El-Shirbeny, M. A., Gad, A., & Zahran, M. B. (2021). Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science, 24(3), 971-981.
Mozumdar, L. (2012). Agricultural productivity and food security in the developing world. Bangladesh Journal of Agricultural Economics, 35(454-2016-36350), 53-69.
Neethirajan, S., & Kemp, B. (2021). Digital twins in livestock farming. Animals, 11(4), 1008.
Pakseresht, A., Ahmadi Kaliji, S., & Xhakollari, V. (2022). How blockchain facilitates the transition toward circular economy in the food chain? Sustainability, 14(18), 11754.
Patel, A., Mahore, A., Nalawade, R. D., Upadhyay, A., & Choudhary, V. (2023). Advancements in Precision Agriculture: Harnessing the Power of Artificial Intelligence and Drones in Indian Agriculture. World Environment Day, 43.
Prabhu, S. S., Kumar, A. V., Murugesan, R., Saha, J., & Dasgupta, I. (2021). Adoption of precision agriculture by detecting and spraying herbicide using UAV. Basrah Journal of Agricultural Sciences, 34, 21-33.
Prause, L. (2021). Digital agriculture and labor: A few challenges for social sustainability. Sustainability, 13(11), 5980.
Raucci, A., Miglione, A., Lenzi, L., Fabbri, P., Di Tocco, J., Massaroni, C., Presti, D. L., Schena, E., Pifferi, V., & Falciola, L. (2023). Characterization and application of porous PHBV-based bacterial polymers to realize novel bio-based electroanalytical (bio) sensors. Sensors and Actuators B: Chemical, 379, 133178.
Rayna, T., & Striukova, L. (2016). From rapid prototyping to home fabrication: How 3D printing is changing business model innovation. Technological Forecasting and Social Change, 102, 214-224.
Raza, Z., Haq, I. U., & Muneeb, M. (2023). Agri-4-All: A Framework for Blockchain Based Agricultural Food Supply Chains in the Era of Fourth Industrial Revolution. IEEE Access, 11, 29851-29867.
Rojas, L. F., Zapata, P., & Ruiz-Tirado, L. (2022). Agro-industrial waste enzymes: Perspectives in circular economy. Current Opinion in Green and Sustainable Chemistry, 34, 100585.
Ronaghi, M. H. (2021). A blockchain maturity model in agricultural supply chain. Information Processing in Agriculture, 8(3), 398-408.
Rowan, N. J. (2023). The role of digital technologies in supporting and improving fishery and aquaculture across the supply chain–Quo Vadis? Aquaculture and Fisheries, 8(4), 365-374.
Shafi, U., Mumtaz, R., García-Nieto, J., Hassan, S. A., Zaidi, S. A. R., & Iqbal, N. (2019). Precision agriculture techniques and practices: From considerations to applications. Sensors, 19(17), 3796.
Soledispa-Cañarte, B. J., Pibaque-Pionce, M. S., Merchán-Ponce, N. P., Alvarez, D. C. M., Tovar-Quintero, J., Escobar-Molina, D. F., Cedeño-Ramirez, J. D., & Rincon-Guio, C. (2023). The Role of Logistics 4.0 in Agribusiness Sustainability and Competitiveness, A Bibliometric and Systematic Literature Review.
Spanaki, K., Sivarajah, U., Fakhimi, M., Despoudi, S., & Irani, Z. (2022). Disruptive technologies in agricultural operations: A systematic review of AI-driven AgriTech research. Annals of Operations Research, 308(1-2), 491-524.
Taneja, A., Nair, G., Joshi, M., Sharma, S., Sharma, S., Jambrak, A. R., Roselló-Soto, E., Barba, F. J., Castagnini, J. M., & Leksawasdi, N. (2023). Artificial Intelligence: Implications for the Agri-Food Sector. Agronomy, 13(5), 1397.
Thilakarathne, N. N., Bakar, M. S. A., Abas, P. E., & Yassin, H. (2022). A cloud enabled crop recommendation platform for machine learning-driven precision farming. Sensors, 22(16), 6299.
Trigona, C., Puglisi, I., Baglieri, A., & Gueli, A. M. (2023). Measurement of Visible Radiation through a Sansevieria cylindrica-Based “Living Sensor”. Applied Sciences, 13(6), 3896.
Xiong, H., Dalhaus, T., Wang, P., & Huang, J. (2020). Blockchain technology for agriculture: applications and rationale. frontiers in Blockchain, 3, 7.
Yadav, S., Kaushik, A., Sharma, M., & Sharma, S. (2022). Disruptive technologies in smart farming: an expanded view with sentiment analysis. AgriEngineering, 4(2), 424-460.
Yoha, K. S., & Moses, J. A. (2023). 3D Printing Approach to Valorization of Agri-Food Processing Waste Streams. Foods, 12(1), 212.
Abd El Kareem Gomaa, H. (2022). Modern Trends in the Development of Smart Agriculture Projects. International Journal of Modern Agriculture and Environment, 2(1), 33-44.
Altarturi, H. H., Nor, A. R. M., Jaafar, N. I., & Anuar, N. B. (2023). A bibliometric and content analysis of technological advancement applications in agricultural e-commerce. Electronic Commerce Research, 1-44.
Anitha, J., & Saranya, N. (2022). Cassava leaf disease identification and detection using deep learning approach. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 17(2).
Beltrán-Lugo, L., Izaguirre-Díaz de León, F., Peinado-Guevara, V., Peinado-Guevara, H., Herrera-Barrientos, J., Cuadras-Berrelleza, A. A., & Montoya-Leyva, M. Á. (2023). Sustainable Innovation Management in the Shrimp Sector of the Municipality of Guasave, State of Sinaloa, Mexico. Sustainability, 15(4), 3161.
Chandan, A., John, M., & Potdar, V. (2023). Achieving UN SDGs in Food Supply Chain Using Blockchain Technology. Sustainability, 15(3), 2109.
Chhetri, T. R., Hohenegger, A., Fensel, A., Kasali, M. A., & Adekunle, A. A. (2023). Towards improving prediction accuracy and user-level explainability using deep learning and knowledge graphs: A study on cassava disease. Expert Systems with Applications, 233, 120955.
Della Pelle, F., Bukhari, Q. U. A., Diduk, R. A., Scroccarello, A., Compagnone, D., & Merkoçi, A. (2023). Freestanding laser-induced two dimensional heterostructures for self-contained paper-based sensors. Nanoscale, 15(15), 7164-7175.
Enescu, F. M., Bizon, N., Onu, A., Răboacă, M. S., Thounthong, P., Mazare, A. G., & Șerban, G. (2020). Implementing blockchain technology in irrigation systems that integrate photovoltaic energy generation systems. Sustainability, 12(4), 1540.
Fennimore, S. A., & Cutulle, M. (2019). Robotic weeders can improve weed control options for specialty crops. Pest management science, 75(7), 1767-1774.
Gomes, J., Esteves, I., Neto, V. V. G., David, J. M. N., Braga, R., Arbex, W., Kassab, M., & de Oliveira, R. F. (2023). A scientific software ecosystem architecture for the livestock domain. Information and Software Technology, 160, 107240.
Henchion, M. M., Regan, Á., Beecher, M., & MackenWalsh, Á. (2022). Developing ‘Smart’Dairy Farming Responsive to Farmers and Consumer-Citizens: A Review. Animals, 12(3), 360.
Jansing, J., Schiermeyer, A., Schillberg, S., Fischer, R., & Bortesi, L. (2019). Genome editing in agriculture: technical and practical considerations. International journal of molecular sciences, 20(12), 2888.
Jung, J., Maeda, M., Chang, A., Bhandari, M., Ashapure, A., & Landivar-Bowles, J. (2021). The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in Biotechnology, 70, 15-22.
Kartika, B., Parson, S. W., Kassim, Z., & Chowdhury, A. J. K. (2022). Overview of halal freshwater aquaculture system: Malaysian perspectives. Water Conservation & Management, 6(1), 01-05.
Kaur, H. (2021). Modelling internet of things driven sustainable food security system. Benchmarking: An International Journal, 28(5), 1740-1760.
Kour, V. P., & Arora, S. (2020). Recent developments of the internet of things in agriculture: a survey. IEEE Access, 8, 129924-129957.
Lachman, J., & López, A. (2022). The nurturing role of the local support ecosystem in the development of the Agtech sector in Argentina. Journal of Agribusiness in Developing and Emerging Economies, 12(4), 714-729.
Mahmud, M. S. A., Abidin, M. S. Z., Emmanuel, A. A., & Hasan, H. S. (2020). Robotics and automation in agriculture: present and future applications. Applications of Modelling and Simulation, 4, 130-140.
Masud, M. M., Azam, M. N., Mohiuddin, M., Banna, H., Akhtar, R., Alam, A. F., & Begum, H. (2017). Adaptation barriers and strategies towards climate change: Challenges in the agricultural sector. Journal of Cleaner Production, 156, 698-706.
Mohamed, E. S., Belal, A., Abd-Elmabod, S. K., El-Shirbeny, M. A., Gad, A., & Zahran, M. B. (2021). Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science, 24(3), 971-981.
Mozumdar, L. (2012). Agricultural productivity and food security in the developing world. Bangladesh Journal of Agricultural Economics, 35(454-2016-36350), 53-69.
Neethirajan, S., & Kemp, B. (2021). Digital twins in livestock farming. Animals, 11(4), 1008.
Pakseresht, A., Ahmadi Kaliji, S., & Xhakollari, V. (2022). How blockchain facilitates the transition toward circular economy in the food chain? Sustainability, 14(18), 11754.
Patel, A., Mahore, A., Nalawade, R. D., Upadhyay, A., & Choudhary, V. (2023). Advancements in Precision Agriculture: Harnessing the Power of Artificial Intelligence and Drones in Indian Agriculture. World Environment Day, 43.
Prabhu, S. S., Kumar, A. V., Murugesan, R., Saha, J., & Dasgupta, I. (2021). Adoption of precision agriculture by detecting and spraying herbicide using UAV. Basrah Journal of Agricultural Sciences, 34, 21-33.
Prause, L. (2021). Digital agriculture and labor: A few challenges for social sustainability. Sustainability, 13(11), 5980.
Raucci, A., Miglione, A., Lenzi, L., Fabbri, P., Di Tocco, J., Massaroni, C., Presti, D. L., Schena, E., Pifferi, V., & Falciola, L. (2023). Characterization and application of porous PHBV-based bacterial polymers to realize novel bio-based electroanalytical (bio) sensors. Sensors and Actuators B: Chemical, 379, 133178.
Rayna, T., & Striukova, L. (2016). From rapid prototyping to home fabrication: How 3D printing is changing business model innovation. Technological Forecasting and Social Change, 102, 214-224.
Raza, Z., Haq, I. U., & Muneeb, M. (2023). Agri-4-All: A Framework for Blockchain Based Agricultural Food Supply Chains in the Era of Fourth Industrial Revolution. IEEE Access, 11, 29851-29867.
Rojas, L. F., Zapata, P., & Ruiz-Tirado, L. (2022). Agro-industrial waste enzymes: Perspectives in circular economy. Current Opinion in Green and Sustainable Chemistry, 34, 100585.
Ronaghi, M. H. (2021). A blockchain maturity model in agricultural supply chain. Information Processing in Agriculture, 8(3), 398-408.
Rowan, N. J. (2023). The role of digital technologies in supporting and improving fishery and aquaculture across the supply chain–Quo Vadis? Aquaculture and Fisheries, 8(4), 365-374.
Shafi, U., Mumtaz, R., García-Nieto, J., Hassan, S. A., Zaidi, S. A. R., & Iqbal, N. (2019). Precision agriculture techniques and practices: From considerations to applications. Sensors, 19(17), 3796.
Soledispa-Cañarte, B. J., Pibaque-Pionce, M. S., Merchán-Ponce, N. P., Alvarez, D. C. M., Tovar-Quintero, J., Escobar-Molina, D. F., Cedeño-Ramirez, J. D., & Rincon-Guio, C. (2023). The Role of Logistics 4.0 in Agribusiness Sustainability and Competitiveness, A Bibliometric and Systematic Literature Review.
Spanaki, K., Sivarajah, U., Fakhimi, M., Despoudi, S., & Irani, Z. (2022). Disruptive technologies in agricultural operations: A systematic review of AI-driven AgriTech research. Annals of Operations Research, 308(1-2), 491-524.
Taneja, A., Nair, G., Joshi, M., Sharma, S., Sharma, S., Jambrak, A. R., Roselló-Soto, E., Barba, F. J., Castagnini, J. M., & Leksawasdi, N. (2023). Artificial Intelligence: Implications for the Agri-Food Sector. Agronomy, 13(5), 1397.
Thilakarathne, N. N., Bakar, M. S. A., Abas, P. E., & Yassin, H. (2022). A cloud enabled crop recommendation platform for machine learning-driven precision farming. Sensors, 22(16), 6299.
Trigona, C., Puglisi, I., Baglieri, A., & Gueli, A. M. (2023). Measurement of Visible Radiation through a Sansevieria cylindrica-Based “Living Sensor”. Applied Sciences, 13(6), 3896.
Xiong, H., Dalhaus, T., Wang, P., & Huang, J. (2020). Blockchain technology for agriculture: applications and rationale. frontiers in Blockchain, 3, 7.
Yadav, S., Kaushik, A., Sharma, M., & Sharma, S. (2022). Disruptive technologies in smart farming: an expanded view with sentiment analysis. AgriEngineering, 4(2), 424-460.
Yoha, K. S., & Moses, J. A. (2023). 3D Printing Approach to Valorization of Agri-Food Processing Waste Streams. Foods, 12(1), 212.
Published
2024-03-31
How to Cite
WEE, Gabriel Wei En; SHOU HUI, Irving Ting.
Harnessing Disruptive Technologies for Agricultural Revolution: A Systematic Literature Review on Impact and Sustainability.
International Journal of Business and Technology Management, [S.l.], v. 6, n. 1, p. 413-424, mar. 2024.
ISSN 2682-7646.
Available at: <https://myjms.mohe.gov.my/index.php/ijbtm/article/view/25859>. Date accessed: 11 sep. 2024.
Section
English Section
