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

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review

“…According to the reviewed literature summarized in Table 2 , smart farming as an implementation of agriculture 4.0 [ 19 , 26 , 27 , 28 , 29 , 45 , 63 , 73 , 75 , 76 , 77 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ] includes smart irrigation strategies by monitoring and controlling various farming stages [ 28 ], implementing data acquisition techniques [ 27 ] for automatically controlling actuator systems in agriculture or even in high-tech data-driven greenhouses [ 79 ]. Furthermore, via simulation, monitoring, controlling, coordinating, and executing farm operations at agricultural sites [ 87 ], DTs improve predictive control in precision irrigation [ 28 , 73 , 88 ], greenhouse horticulture, and organic vegetable and livestock farming.…”

“…Furthermore, via simulation, monitoring, controlling, coordinating, and executing farm operations at agricultural sites [ 87 ], DTs improve predictive control in precision irrigation [ 28 , 73 , 88 ], greenhouse horticulture, and organic vegetable and livestock farming. Smart farming enhances remote detection and monitoring of vegetation and crop stress in agriculture [ 86 ], developing DT stages and forecasting plant yield in greenhouses, vertical farms, or outdoor fields [ 76 , 77 ], or even in urban and indoor farming, of vertical agriculture utilizing hydroponics, aeroponics, aquaculture, and aquaponics. Smart farming approaches the smart agriculture [ 85 ] as well as the sustainable agriculture 4.0 [ 80 ] sector to address precision farming and management [ 75 , 76 ] via the adoption of digital technologies and techniques in agriculture [ 27 , 29 , 64 ], leading the way to digital representations of plants [ 9 ], inventory quality in agriculture, greenhouse production flow, and food safety and quality within the food supply chain [ 45 ].…”

“…Smart farming enhances remote detection and monitoring of vegetation and crop stress in agriculture [ 86 ], developing DT stages and forecasting plant yield in greenhouses, vertical farms, or outdoor fields [ 76 , 77 ], or even in urban and indoor farming, of vertical agriculture utilizing hydroponics, aeroponics, aquaculture, and aquaponics. Smart farming approaches the smart agriculture [ 85 ] as well as the sustainable agriculture 4.0 [ 80 ] sector to address precision farming and management [ 75 , 76 ] via the adoption of digital technologies and techniques in agriculture [ 27 , 29 , 64 ], leading the way to digital representations of plants [ 9 ], inventory quality in agriculture, greenhouse production flow, and food safety and quality within the food supply chain [ 45 ]. Although the DTs remain a major issue in our research criteria, in papers [ 29 , 45 , 63 , 83 , 84 , 85 ], they are implied as such.…”

“…Remote detection and monitoring of vegetation and crop stress in agriculture seem feasible based on [ 86 ] utilizing LiDAR—stereo-photogrammetry technology, using multi-spectral imagery, passive microwave remote sensing, RFID schemes [ 9 , 45 ], active microwave remote sensing (RADAR) and sensors onboard UAVs and satellites [ 45 , 76 , 88 ]. Location determination in farming via geospatial position (GPS) plays a significant role in the remote detection and monitoring of vegetation and crop conditions in agriculture [ 88 ].…”

Is a Top Level Ontology Based Digital Twin the Solution to Human-Machine Interoperability?

“…• Ameri et al [13] in 2010 identified formal ontologies, or TLOs, as the tool that will represent the digital factories of the future. • Skobelev et al [14] used ontology as a fundamental layer for the DT in the field of farming management. Ontology has been used to define the basic classes of concepts and relations, called "explanatory dictionary for agents".…”