References
- Abdul Haleem, S., Kshirsagar, P. R., Manoharan, H., Prathap, B., Pradeep Kumar, K., Tirth, V., Islam, S., Katragadda, R., & Amibo, T. A. (2022). Wireless sensor data acquisition and control monitoring model for internet of things applications. Scientific Programming, 2022. doi: 10.1155/2022/9099163
- Akhter, R., & Sofi, S. A. (2021). Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University-Computer and Information Sciences. doi: 10.1016/j.jksuci.2021.05.013
- Ali, A. M., Abouelghar, M. A., Belal, A. A., Saleh, N., Younes, M., Selim, A., Amin, M. E. S., Elwesemy, A., Kucher, D. E., Magignan, S., & Savin, I. (2022). Crop Yield Prediction Using Multi Sensors Remote Sensing. The Egyptian Journal of Remote Sensing and Space Science. doi: 10.1016/j.ejrs.2022.04.006
- Alves, R. G., Maia, R. F., & Lima, F. (2023). Development of a Digital Twin for smart farming: Irrigation management system for water saving. Journal of Cleaner Production, 388, 135920. doi: 10.1016/j. jclepro.2023.135920
- Anderson, N. T., Walsh, K. B., Koirala, A., Wang, Z., Amaral, M. H., Dickinson, G. R., Sinha, P., & Robson, A. J. (2021). Estimation of Fruit Load in Australian Mango Orchards Using Machine Vision. Agronomy, 11(9), 1711. doi: 10.3390/agronomy11091711
- Balestrieri, E., Daponte, P., De Vito, L., & Lamonaca, F. (2021). Sensors and measurements for unmanned systems: An overview. Sensors, 21(4), 1518. doi: 10.3390/s21041518
- Botín-Sanabria, D. M., Mihaita, A. S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., & Lozoya-Santos, J. D. J. (2022). Digital twin technology challenges and applications: A comprehensive review. Remote Sensing, 14(6), 1335. doi: 10.3390/rs14061335
- Chaux, J. D., Sanchez-Londono, D., & Barbieri, G. (2021). A digital twin architecture to optimize productivity within controlled environment agriculture. Applied Sciences, 11(19), 8875. doi: 10.3390/app11198875
- Chen, C. J., Huang, Y. Y., Li, Y. S., Chen, Y. C., Chang, C. Y., & Huang, Y. M. (2021a). Identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying. IEEE Access, 9, 21986-21997. doi: 10.1109/ACCESS.2021.3056082
- Chen, W., Zhang, J., Guo, B., Wei, Q., & Zhu, Z. (2021b). An Apple Detection Method Based on Des-YOLO v4 Algorithm for Harvesting Robots in Complex Environment. Mathematical Problems in Engineering, 2021. doi: 10.1155/2021/7351470
- De Alwis, S., Hou, Z., Zhang, Y., Na, M. H., Ofoghi, B., & Sajjanhar, A. (2022). A survey on smart farming data, applications and techniques. Computers in Industry, 138, 103624. doi: 10.1016/j.compind.2022.103624
- Di Gennaro, S. F., Nati, C., Dainelli, R., Pastonchi, L., Berton, A., Toscano, P., & Matese, A. (2020). An automatic UAV based segmentation approach for pruning biomass estimation in irregularly spaced chestnut orchards. Forests, 11(3), 308. doi: 10.3390/f11030308
- European Commission. (2019). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. The European Green Deal. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1576150542719&uri=COM%3A2019%3A640%3AFIN
- European Commission. (2020). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. EU Biodiversity Strategy for 2030. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1590574123338&uri=CELEX:52020DC0380
- Fisch, C., & Block, J. (2018). Six tips for your (systematic) literature review in business and management research. Management Review Quarterly, 68(2), 103-106. doi: 10.1007/s11301-018-0142-x
- Fu, L., Wu, F., Zou, X., Jiang, Y., Lin, J., Yang, Z., & Duan, J. (2022). Fast detection of banana bunches and stalks in the natural environment based on deep learning. Computers and Electronics in Agriculture, 194, 106800. doi: 10.1016/j.compag.2022.106800
- Gao, F., Fang, W., Sun, X., Wu, Z., Zhao, G., Li, G., Li, R., Fu, L., & Zhang, Q. (2022). A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern orchard. Computers and Electronics in Agriculture, 197, 107000. doi: 10.1016/j.compag.2022.107000
- Gao, P., Xie, J., Yang, M., Zhou, P., Chen, W., Liang, G., Chen, Y., Han, X., & Wang, W. (2021). Improved soil moisture and electrical conductivity prediction of citrus orchards based on IOT using Deep Bidirectional LSTM. Agriculture, 11(7), 635. doi: 10.3390/ agriculture11070635
- Grand View Research. (2022). Artificial Intelligence Market Size Report, 2022-2030. Retrieved from https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market
- Hasan, R. I., Yusuf, S. M., & Alzubaidi, L. (2020). Review of the state of the art of deep learning for plant diseases: a broad analysis and discussion. Plants, 9(10), 1302. doi: 10.3390/plants9101302
- Henrichs, E., Noack, T., Pinzon Piedrahita, A. M., Salem, M. A., Stolz, J., & Krupitzer, C. (2021). Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry. Sensors, 22(1), 115. doi: 10.3390/ s22010115
- Hui, K. K. W., Wong, M. S., Kwok, C. Y. T., Li, H., Abbas, S., & Nichol, J. E. (2022). Unveiling Falling Urban Trees before and during Typhoon Higos (2020): Empirical Case Study of Potential Structural Failure Using Tilt Sensor. Forests, 13(2), 359. doi: doi. org/10.3390/f13020359
- Jafarbiglu, H., & Pourreza, A. (2022). A comprehensive review of remote sensing platforms, sensors, and applications in nut crops. Computers and Electronics in Agriculture, 197, 106844. doi: 10.1016/j.compag.2022.106844
- Jerhamre, E., Carlberg, C. J. C., & van Zoest, V. (2022). Exploring the susceptibility of smart farming: Identified opportunities and challenges. Smart Agricultural Technology, 2, 100026. doi: 10.1016/j.atech.2021.100026
- Jia, A. (2021). Intelligent garden planning and design based on agricultural internet of things. Complexity, 2021. doi: 10.1155/2021/9970160
- Jin, S., Li, W., Cao, Y., Jones, G., Chen, J., Li, Z., Chang, Q., Yang, G., & Frewer, L. J. (2022). Identifying barriers to sustainable apple production: A stakeholder perspective. Journal of Environmental Management, 302, 114082. doi: 10.1016/j.jenvman.2021.114082
- Kalyanaraman, A., Burnett, M., Fern, A., Khot, L., & Viers, J. (2022). Special report: The AgAID AI institute for transforming workforce and decision support in agriculture. Computers and Electronics in Agriculture, 197, 106944. doi: 10.1016/j.compag.2022.106944
- Kim, S., & Ji, Y. (2018). Gap analysis. The International Encyclopedia of Strategic Communication, 1-6. doi: 10.1002/9781119010722.iesc0079
- Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1-26. Retrieved from https://www.researchgate.net/profile/Barbara-Kitchenham/publication/228756057_Procedures_for_Performing_Systematic_Reviews/links/618cfae961f09877207f8471/Procedures-for-Performing-Systematic-Reviews.pdf
- Koirala, A., Walsh, K. B., Wang, Z., & McCarthy, C. (2019). Deep learning–Method overview and review of use for fruit detection and yield estimation. Computers and Electronics in Agriculture, 162, 219-234. doi: 10.1016/j.compag.2019.04.017
- Kolhalkar, N. R., Krishnan, V. L., Pandit, A. A., Somkuwar, R. G., & Shaaikh, J. A. (2021). Design and performance evaluation of a novel end-effector with integrated gripper cum cutter for harvesting greenhouse produce. International Journal of Advanced Technology and Engineering Exploration, 8(84), 1479. doi: 10.19101/IJATEE.2021.874507
- Kondoyanni, M., Loukatos, D., Maraveas, C., Drosos, C., & Arvanitis, K. G. (2022). Bio-Inspired Robots and Structures toward Fostering the Modernization of Agriculture. Biomimetics, 7(2), 69. doi: 10.3390/biomimetics7020069
- Kun, T., Sanmin, S., Liangzong, D., & Shaoliang, Z. (2021). Design of an Intelligent Irrigation System for a Jujube Orchard based on IoT. INMATEH-Agricultural Engineering, 63(1). doi: 10.35633/inmateh-63-19
- Lee, U., Islam, M. P., Kochi, N., Tokuda, K., Nakano, Y., Naito, H., Kawasaki, Y., Ota, T., Sugiyama, T., & Ahn, D. H. (2022). An Automated, Clip-Type, Small Internet of Things Camera-Based Tomato Flower and Fruit Monitoring and Harvest Prediction System. Sensors, 22(7), 2456. doi: 10.3390/s22072456
- Lemphane, N. J., Kuriakose, R. B., & Kotze, B. (2023). Designing a Digital Shadow for Pasture Management to Mitigate the Impact of Climate Change. In: A. Joshi, M. Mahmud, & R. G. Ragel (Eds.), Information and Communication Technology for Competitive Strategies (ICTCS 2021). Lecture Notes in Networks and Systems, 400. Singapore: Springer. doi: 10.1007/978-981-19-0095-2_35
- Maheswari, P., Raja, P., Apolo-Apolo, O. E., & Pérez-Ruiz, M. (2021). Intelligent fruit yield estimation for orchards using deep learning based semantic segmentation techniques—a review. Frontiers in Plant Science, 12, 684328. doi: 10.3389/fpls.2021.684328
- Mirhaji, H., Soleymani, M., Asakereh, A., & Mehdizadeh, S. A. (2021). Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions. Computers and Electronics in Agriculture, 191, 106533. doi: 10.1016/j.compag.2021.106533
- Mohamed, E. S., Belal, A. 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. 10.1016/j.ejrs.2021.08.007
- Mwinuka, P. R., Mbilinyi, B. P., Mbungu, W. B., Mourice, S. K., Mahoo, H. F., & Schmitter, P. (2021). The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L). Agricultural Water Management, 245, 106584. doi: 10.1016/j.agwat.2020.106584
- Niu, H., Zhao, T., Wang, D., & Chen, Y. (2022). Estimating Evapotranspiration of Pomegranate Trees Using Stochastic Configuration Networks (SCN) and UAV Multispectral Imagery. Journal of Intelligent & Robotic Systems, 104(4), 1-11. doi: 10.1007/s10846-022-01588-2
- Ortenzi, L., Violino, S., Pallottino, F., Figorilli, S., Vasta, S., Tocci, F., Antonucci, F., Imperi, G., & Costa, C. (2021). Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius. Drones, 5(4), 118. doi: 10.3390/drones5040118
- O’Shaughnessy, S. A., Kim, M., Lee, S., Kim, Y., Kim, H., & Shekailo, J. (2021). Towards smart farming solutions in the US and South Korea: A comparison of the current status. Geography and Sustainability. doi: 10.1016/j.geosus.2021.12.002
- Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic Reviews, 10(89). doi: 10.1136/bmj.n71
- Panday, U. S., Pratihast, A. K., Aryal, J., & Kayastha, R. B. (2020). A review on drone-based data solutions for cereal crops. Drones, 4(3), 41. doi: 10.3390/ drones4030041
- Pylianidis, C., Osinga, S., & Athanasiadis, I. N. (2021). Introducing digital twins to agriculture. Computers and Electronics in Agriculture, 184, 105942. doi: 10.1016/j. compag.2020.105942
- Quezada, C., Mercado, M., Bastías, R. M., & Sandoval, M. (2021). Data Validation of Automatic Weather Stations by Temperature Monitoring in Apple Orchards. Chilean Journal of Agricultural & Animal Sciences, 37(1), 21-31. doi: 0.29393/CHJAAS37-3VDCQ40003
- Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980-22012. doi: 10.1109/ ACCESS.2020.2970143
- 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. doi: 10.3390/agronomy12010127
- Skobelev, P., Mayorov, I., Simonova, E., Goryanin, O., Zhilyaev, A., Tabachinskiy, A., & Yalovenko, V. (2021). Development of digital twin of plant for adaptive calculation of development stage duration and forecasting crop yield in a cyber-physical system for managing precision farming. In Cyber-Physical Systems (pp. 83-96). Cham: Springer. doi: 10.1007/978-3-030-67892-0_8
- Sung, Y. M., & Kim, T. (2022). Smart Farm Realization based on Digital Twin. ICIC Express Letters, Part B: Applications, 13(4), 421-427. doi: 10.24507/icicelb.13.04.421
- Tardaguila, J., Stoll, M., Gutiérrez, S., Proffitt, T., & Diago, M. P. (2021). Smart applications and digital technologies in viticulture: A review. Smart Agricultural Technology, 1, 100005. doi: 10.1016/j.atech.2021.100005
- Thapa, A., & Horanont, T. (2022). Digital Twins in Farming with the Implementation of Agricultural Technologies. Applied Geography and Geoinformatics for Sustainable Development: Proceedings of ICGGS 2022, 121-132. doi: 10.1007/978-3-031-16217-6_9
- Toosi, A., Javan, F. D., Samadzadegan, F., Mehravar, S., Kurban, A., & Azadi, H. (2022). Citrus orchard mapping in Juybar, Iran: Analysis of NDVI time series and feature fusion of multi-source satellite imageries. Ecological Informatics, 70, 101733. doi: 10.1016/j.ecoinf.2022.101733
- Van Der Burg, S., Kloppenburg, S., Kok, E. J., & Van Der Voort, M. (2021). Digital twins in agri-food: Societal and ethical themes and questions for further research. NJAS: Impact in Agricultural and Life Sciences, 93(1), 98-125. doi: 10.1080/27685241.2021.1989269
- Verdouw, C., Tekinerdogan, B., Beulens, A., & Wolfert, S. (2021). Digital twins in smart farming. Agricultural Systems, 189, 103046. doi: 10.1016/j. agsy.2020.103046
- Wang, D., & He, D. (2021). Channel pruned YOLO V5s-based deep learning approach for rapid and accurate apple fruitlet detection before fruit thinning. Biosystems Engineering, 210, 271-281. doi: 10.1016/j.biosystemseng.2021.08.015
- Xia, X., Chai, X., Zhang, N., Zhang, Z., Sun, Q., & Sun, T. (2022). Culling Double Counting in Sequence Images for Fruit Yield Estimation. Agronomy, 12(2), 440. doi: 10.3390/agronomy12020440
- Zhang, C., Valente, J., Kooistra, L., Guo, L., & Wang, W. (2021). Orchard management with small unmanned aerial vehicles: A survey of sensing and analysis approaches. Precision Agriculture, 22(6), 2007-2052. doi: 10.1007/s11119-021-09813-y
- Zhang, P., Wang, S., Bai, M., Bai, Q., Chen, Z., Chen, X., Hu, Y., Zhang, J., Li, Y., Hu, X., Shi, Y., & Deng, J. (2022). Intelligent Spraying Water Based on the Internet of Orchard Things and Fuzzy PID Algorithms. Journal of Sensors, 2022. doi: 10.1155/2022/4802280