[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
Grand View Research. (2022). Artificial Intelligence Market Size Report, 2022-2030. Retrieved from https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
Jia, A. (2021). Intelligent garden planning and design based on agricultural internet of things. Complexity, 2021. doi: 10.1155/2021/9970160
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
Kim, S., & Ji, Y. (2018). Gap analysis. The International Encyclopedia of Strategic Communication, 1-6. doi: 10.1002/9781119010722.iesc0079
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
, 