# Applications of Digital Twin across Industries: A Review
## 11 Agriculture  
Enterprises in the agriculture sector are involved in harvesting and producing agricultural commodities such as crops, livestock, poultry, fish, as well as fertilizers, packaged foods, and agricultural machinery, etc. [154]. Due to the growing human population, the agriculture industry is under the extreme burden of increasing its production to meet the nonvisument demands of this record number of human beings. According to the Food and Agriculture Organization of the UN, the global population in just 30 years, i.e., in 2050, will reach 9.8 billion, which will create a demand for food, feed, and biofuel 50% higher than it was in 2012 [155]. To achieve this productivity goal, humans will need access to every tool available. Developing a DT for smart farming can enable sustainable development and increase food security for the global population [33]. Although DT technology is still in the early phases of development in the agriculture industry [34], potential applications in this field have been identified.  
[PERSON] and [PERSON] [156], in the Asian-Australasian Conference on Precision Agriculture, presented potential applications of DTs in different fields within agriculture, such as animal husbandry, apiculture, crop storage, agriculture equipment/machinery, etc. The DT applications explored by them include:  
* Remote monitoring of livestock for the detection and analysis of their health including heat and estrus cycles as wells as the animals' movement tracking.
* Identification of pests or diseases in plants.
* Management and optimization of production plants and replenishment routes by monitoring the stocks of the silos.
* Evaluation of cost-effectiveness of crop management treatments along with tracking machinery in real time.
* Detection and identification of flies in olive farms to use pesticides effectively.
* Monitoring bee colonies for diseases/infections and managing honey storage.  
[PERSON] et al. [157] presented a reference model for the implementation of a DT in vertical farming by enriching the physical system by sensors collecting data on temperature, humidity, luminosity, and the relative CO\({}_{2}\) concentration, which is stored on the cloud. Using intelligent data analysis, the system will be able to suggest methods to plan vertical farms and increase production. A similar framework comprising of modeling and analysis, simulation, big data, and visualization for livestock farms was presented by [PERSON] et al. [158]. Besides managing livestock and farms, the DT can promote sustainability. Agriculture uses 11% of the world's land and 70% of fresh water; it is also responsible for 80% of deforestation [155]. DT technology can help by tracking any changes in carbon emission, biodiversity, pollination, and water catchment services, and their causes which will require the research and development of correct sensors, models, and interfaces for these specific purposes [159]. [PERSON] [160] shows concern over the negligence of the real-world systems due to developments of DTs, which can lead to less empathic and intellectually and emotionally distant farmers. The author suggests that farmers and agricultural organizations should make sure to pay attention to the aspects that are not a part of the digital model while taking advantage of the technology to grow.