Horticulture 4.0: Adoption of Industry 4.0 Technologies in Horticulture for Meeting Sustainable Farming
Source abstract
Abstract
The United Nations emphasized a significant agenda on reducing hunger and protein malnutrition as well as micronutrient (vitamins and minerals) malnutrition, which is estimated to affect the health of up to two billion people. The UN also recognized this need through Sustainable Development Goals (SDG 2 and SDG 12) to end hunger and foster sustainable agriculture by enhancing the production and consumption of fruits and vegetables. Previous studies only stressed the various issues in horticulture with regard to industries, but they did not emphasize the centrality of Industry 4.0 technologies for confronting the diverse issues in horticulture, from production to marketing in the context of sustainability. The current study addresses the significance and application of Industry 4.0 technologies such as the Internet of Things, cloud computing, artificial intelligence, blockchain, and big data for horticulture in enhancing traditional practices for disease detection, irrigation management, fertilizer management, maturity identification, marketing, and supply chain, soil fertility, and weather patterns at pre-harvest, harvest, and post-harvest. On the basis of analysis, the article identifies challenges and suggests a few vital recommendations for future work. In horticulture settings, robotics, drones with vision technology and AI for the detection of pests, weeds, plant diseases, and malnutrition, and edge-computing portable devices that can be developed with IoT and AI for predicting and estimating crop diseases are vital recommendations suggested in the study.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Agriculture, Uttaranchal University, Dehradun 248007, Uttarakhand, India
Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, CP, Mexico
Law College Dehradun, Uttaranchal University, Dehradun 248007, Uttarakhand, India
Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, West Bengal, India
Digital Transformation Portfolio, Tshwane University of Technology, Staatsartillerie Rd, Pretoria West, Pretoria 0183, South Africa
Funding
Tshwane University of Technology, South Africa
References (90)
Show source-provided references
1. (2022, September 06). UN Report: Global Hunger Numbers Rose to as Many as 828 Million in 2021. Available online: https://www.fao.org/newsroom/detail/un-report-global-hunger-SOFI-2022-FAO/en.
2. (2022, September 06). Goal 2: Zero Hunger-United Nations Sustainable Development. Available online: https://www.un.org/sustainabledevelopment/hunger/.
3. (2022, September 06). Goal 12. Available online: https://sdgs.un.org/goals/goal12.
4. Keatinge, J.D.H., Virchow, D., and Schreinemachers, P. (2016, January 20). Horticulture for sustainable development: Evidence for impact of international vegetable research and development. Proceedings of the International Symposia on Tropical and Temperate Horticulture-ISTTH2016 1205, Cairns, Australia.
5. Qingxue, L., and Wu, H. (2016). Research on vegetable growth monitoring platform based on facility agricultural IoT. International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, Springer.
6. Cappelli, I., Fort, A., Pozzebon, A., Tani, M., Trivellin, N., Vignoli, V., and Bruzzi, M. (2022). Autonomous IoT Monitoring Matching Spectral Artificial Light Manipulation for Horticulture. Sensors, 22.10.3390/s22114046 ↗
7. Lamm, K., Powell, A., and Lombardini, L. (2021). Identifying Critical Issues in the Horticulture Industry: A Delphi Analysis during the COVID-19 Pandemic. Horticulturae, 7.10.3390/horticulturae7110416 ↗
8. Review on: Role of robotics in horticulture
9. Applications of deep-learning approaches in horticultural research: A review10.1038/s41438-021-00560-9 ↗
10. Digital Twins in greenhouse horticulture: A review10.1016/j.compag.2022.107183 ↗
11. Internet of Things and autonomous control for vertical cultivation walls towards smart food growing: A review10.1016/j.ufug.2021.127094 ↗
12. Longchamps, L., Tisseyre, B., Taylor, J., Sagoo, L., Momin, A., Fountas, S., Manfrini, L., Ampatzidis, Y., Schueller, J.K., and Khosla, R. (2022). Yield sensing technologies for perennial and annual horticultural crops: A review. Precis. Agric., 1–42.10.1007/s11119-022-09906-2 ↗
13. Jallow, M.F.A., Awadh, D.G., Albaho, M.S., Devi, V.Y., and Ahmad, N. (2017). Monitoring of Pesticide Residues in Commonly Used Fruits and Vegetables in Kuwait. Int. J. Environ. Res. Public Health, 14.10.3390/ijerph14080833 ↗
14. Bulgari, R., Petrini, A., Cocetta, G., Nicoletto, C., Ertani, A., Sambo, P., Ferrante, A., and Nicola, S. (2021). The Impact of COVID-19 on Horticulture: Critical Issues and Opportunities Derived from an Unexpected Occurrence. Horticulturae, 7.10.3390/horticulturae7060124 ↗
15. Rehman, M.U., Rather, G.H., Gull, Y., Mir, M.R., Mir, M.M., Waida, U.I., and Hakeem, K.R. Effect of climate change on horticultural crops. Crop Production and Global Environmental Issues, Springer.
16. Bandh, S.A. (2022). Impacts of Climate Change on Agriculture and Horticulture. Climate Change, Springer.10.1007/978-3-030-86290-9 ↗
17. Evaluation of Asparagus adscendens accessions for root yield and shatavarin IV content in India10.3906/tar-2006-42 ↗
18. Effect of seed priming on horticultural crops10.1016/j.scienta.2021.110197 ↗
19. Evaluation of Lonicera caerulea L. genotypes based on morphological characteristics offruits germplasm collection10.3906/tar-2002-14 ↗
20. Mall, M., Kumar, R., and Akhtar, M.Q. (2021). Horticultural crops and abiotic stress challenges. Stress Tolerance in Horticultural Crops, Woodhead Publishing.10.1016/B978-0-12-822849-4.00001-2 ↗
21. Alireza, A., Dennis, R., and Ludena, A. (2014, January 7–9). Big data application to the vegetable production and distribution system. Proceedings of the 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, Kuala Lumpur, Malaysia.
22. A design of IoT-based monitoring system for intelligence indoor micro-climate horticulture farming in Indonesia10.1016/j.procs.2019.09.001 ↗
23. Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues10.1016/j.comnet.2018.07.017 ↗
24. Internet of things (IoT): A review of its enabling technologies in healthcare applications, standards protocols, security, and market opportunities10.1109/JIOT.2021.3062630 ↗
25. A comparative study of LPWAN technologies for large-scale IoT deployment10.1016/j.icte.2017.12.005 ↗
26. Arya, P.S., and Gangwar, M. (2021, January 17–18). A Proposed Architecture: Detecting Freshness of Vegetables using Internet of Things (IoT) & Deep Learning Prediction Algorithm. Proceedings of the 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India.10.1109/ICAC3N53548.2021.9725428 ↗
27. Study on artificial intelligence: The state of the art and future prospects
28. Sagiroglu, S., and Sinanc, D. (2013, January 20–24). Big data: A review. Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA.10.1109/CTS.2013.6567202 ↗
29. Big data and IoT-based applications in smart environments: A systematic review10.1016/j.cosrev.2020.100318 ↗
30. Blockchain technology applications for Industry 4.0: A literature-based review10.1016/j.bcra.2021.100027 ↗
31. Alireza, A., and Ludena, R.D.A. (2013, January 3–4). Big Data approach to a novel nutrition-based vegetable production and distribution system. Proceedings of the 2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM), Yogyakarta, Indonesia.
32. Kumar, P.C., and Bhatnagar, R. (2020). Social internet of things in agriculture: An overview and future scope. Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Springer.
33. Sun, Z., Hui, X., and Wensheng, W. (2010). An architecture for the agricultural machinery intelligent scheduling in cross-regional work based on cloud computing and internet of things. International Conference on Computer and Computing Technologies in Agriculture, Springer.
34. Big data in agriculture: A challenge for the future10.1093/aepp/ppx056 ↗
35. Big data in agriculture and nutrition
36. The rise of blockchain technology in agriculture and food supply chains10.1016/j.tifs.2019.07.034 ↗
37. Codeluppi, G., Cilfone, A., Davoli, L., and Ferrari, G. (2019, January 24–26). VegIoT garden: A modular IoT management platform for urban vegetable gardens. Proceedings of the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Portici, Italy.10.1109/MetroAgriFor.2019.8909228 ↗
38. Downy mildew: A serious disease threat to rose health worldwide10.1094/PDIS-12-17-1968-FE ↗
39. The design and realization of apple orchard intelligent monitoring system based on internet of things technology10.4028/www.scientific.net/AMR.546-547.898 ↗
40. Zhang, H., He, L., Di Gioia, F., Choi, D.D., and Heinemann, P. (2021). Internet of things (IoT)-based precision irrigation with LoRaWAN technology applied to vegetable production. 2021 ASABE Annual International Virtual Meeting, American Society of Agricultural and Biological Engineers.10.13031/aim.202100877 ↗
41. Environmental monitoring and disease detection of plants in smart greenhouse using internet of things10.1088/2399-6528/ab90c1 ↗
42. Sensor Systems for Greenhouse Microclimate Monitoring and Control: A Review10.1007/s42853-020-00075-6 ↗
43. Utilization of IoT for soil moisture and temperature monitoring system for onion growth10.28991/esj-2021-SP1-07 ↗
44. An IoT-based greenhouse monitoring system with Micaz motes10.1016/j.procs.2017.08.300 ↗
45. Dennis, A.L.R., Ahrary, A., Horibe, N., and Yang, W.S. (September, January 31). IoT-security approach analysis for the novel nutrition-based vegetable production and distribution system. Proceedings of the 3rd IIAI International Conference on Advanced Applied Informatics, IIAI-AAI 2014, Kokura Kita-ku, Japan.
46. Jie, Y., Zhang, X., Lu, Q., Xin, C., Liu, C., and Chen, Z. (2012). IOT based provenance platform for vegetables supplied to Hong Kong. Recent Advances in Computer Science and Information Engineering, Springer.
47. Smart refrigerator and vegetable identification system using image processing and IOT
48. Apple Sweetness Measurement and Fruit Disease Prediction Using Image Processing Techniques Based on Human-Computer Interaction for Industry 4.0
49. Performance analysis of soft computing techniques for the automatic classification of fruits dataset10.1007/s00500-019-03776-z ↗
50. John, K., Haw, S., Su, N., and Mulaafer, S. (2021, January 13–15). Kiwi Fruit IoT Shelf Life Estimation During Transportation with Cloud Computing. Proceedings of the 2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia.
51. Post-Harvest Grading Of Carica Papaya Fruit Using Image Segmentation And Soft Computing
52. Yaya, W., and Nugraha, E. (2020, January 21–22). Development Mobile Learning For Vegetable Farming In Indonesia Based On Mobile Cloud Computing. Proceedings of the 2020 6th International Conference on Science in Information Technology (ICSITech), Palu, Indonesia.10.1109/ICSITech49800.2020.9392074 ↗
53. Narendra, V.G., and Ancilla, J.P. (2020). Defects Detection in Fruits and Vegetables Using Image Processing and Soft Computing Techniques. International Conference on Harmony Search Algorithm, Springer.10.1007/978-981-15-8603-3_29 ↗
54. Silke, H., de Zwart, F., Elings, A., Petropoulou, A., and Righini, I. (2020). Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality. Sensors, 20.10.3390/s20226430 ↗
55. Kingsley, J., Isong, I.A., Kebonye, N.M., Ayito, E.O., Agyeman, P.C., and Afu, S.M. (2020). Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil. Land, 9.10.3390/land9120487 ↗
56. Yousef, A.-G., Aghabara, A., Davari, M., and Maja, J.M. (2022). Feasibility of Using Computer Vision and Artificial Intelligence Techniques in Detection of Some Apple Pests and Diseases. Appl. Sci., 12.10.3390/app12020906 ↗
57. Machine learning and remote sensing techniques applied to estimate soil indicators–Review10.1016/j.ecolind.2021.108517 ↗
58. Determining the geographical origin of lettuce with data mining applied to micronutrients and soil properties10.1590/1678-992x-2020-0011 ↗
59. Artificial intelligence assisted technologies for controlling the drying of fruits and vegetables using physical fields: A review10.1016/j.tifs.2020.08.015 ↗
60. CLadron*: AI assisted device for identifying artificially ripened climacteric fruits10.1016/j.procs.2020.04.069 ↗
61. Bittu, S., Aziz, N.A., and Siddiqi, M.A.M. (2019, January 18–19). AI Based Robotic Systems for the quality control of Date Palm Fruits-A Review. Proceedings of the 2019 International Conference on Digitization (ICD), Sharjah, United Arab Emirates.
62. Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data10.1038/s41598-018-25055-x ↗
63. Fully convolutional networks for blueberry bruising and calyx segmentation using hyperspectral transmittance imaging10.1016/j.biosystemseng.2020.01.018 ↗
64. Wang, Z., Hu, M., and Zhai, G. (2018). Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data. Sensors, 18.10.3390/s18041126 ↗
65. Blueberry bruise detection by pulsed thermographic imaging10.1016/j.postharvbio.2017.10.011 ↗
66. Assessment of strawberry ripeness using hyperspectral imaging10.1080/00032719.2020.1812622 ↗
67. Analysis and detection of decayed blueberry by low field nuclear magnetic resonance and imaging10.1016/j.postharvbio.2019.110951 ↗
68. Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing10.1109/JSEN.2019.2949528 ↗
69. An enhanced deep learning approach in forecasting banana harvest yields
70. Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield10.1038/s41438-020-0323-3 ↗
71. Deep learning for tomato diseases: Classification and symptoms visualization10.1080/08839514.2017.1315516 ↗
72. Deep Learning Approach for Apple Edge Detection to Remotely Monitor Apple Growth in Orchards10.1109/ACCESS.2020.2971524 ↗
73. Kang, H., and Chen, C. (2019). Fruit detection and segmentation for apple harvesting using visual sensor in orchards. Sensors, 19.10.3390/s19204599 ↗
74. Fast implementation of real-time fruit detection in apple orchards using deep learning10.1016/j.compag.2019.105108 ↗
75. Csillik, O., Cherbini, J., Johnson, R., Lyons, A., and Kelly, M. (2018). Identification of citrus trees from unmanned aerial vehicle imagery using convolutional neural networks. Drones, 2.10.3390/drones2040039 ↗
76. Rafael, P., and Shi, E. (2017, January 25–27). Fruitchains: A fair blockchain. Proceedings of the ACM Symposium on Principles of Distributed Computing, Washington, DC, USA.
77. Blockchain: An emerging novel technology to upgrade the current fresh fruit supply chain10.1016/j.tifs.2022.03.030 ↗
78. Arsenii, S. (2019, January 4–5). Traceability technology: Fruits and vegetables trader case study. Proceedings of the International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI 2019), St. Petersburg, Russia.
79. Sharif, K.I.M., Hassan, M.G., Miraz, M.H., Zulkifly, E., Udin, Z.M., and Omar, M. (2021). Factors Affecting Blockchain in Fruit Retail Market: An Unveiling Myth of Blockchain. Intelligent Manufacturing and Mechatronics, Springer.10.1007/978-981-16-0866-7_97 ↗
80. Zhang, C., Cai, J., Xiao, D., Ye, Y., and Chehelamirani, M. (2018). Research on vegetable pest warning system based on multidimensional big data. Insects, 9.10.3390/insects9020066 ↗
81. Agriculture drones: A modern breakthrough in precision agriculture
82. Tripicchio, P., Satler, M., Dabisias, G., Ruffaldi, E., and Avizzano, C.A. (2015, January 15–17). Towards smart farming and sustainable agriculture with drones. Proceedings of the 2015 International Conference on Intelligent Environments, Prague, Czech Republic.10.1109/IE.2015.29 ↗
83. Internet of Drones10.1109/ACCESS.2016.2537208 ↗
84. Towards a science of integrated AI and Robotics10.1016/j.artint.2017.03.003 ↗
85. Blockchain technology in food supply chains: Review and bibliometric analysis10.1016/j.techsoc.2022.101954 ↗
86. Mobile Edge Computing: A Survey10.1109/JIOT.2017.2750180 ↗
87. Edge computing: Vision and challenges10.1109/JIOT.2016.2579198 ↗
88. The Emergence of Edge Computing10.1109/MC.2017.9 ↗
89. A Survey of Recent Advances in Edge-Computing-Powered Artificial Intelligence of Things10.1109/JIOT.2021.3088875 ↗
90. Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning10.1007/s11227-020-03288-w ↗