Agricultural cyber physical system collaboration for greenhouse stress management

Guo, Ping; Dusadeerungsikul, Puwadol Oak; Nof, Shimon Y.

doi:10.1016/j.compag.2018.05.022

Cited by 46 publications

(17 citation statements)

References 25 publications

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“…Precision agriculture can rely on quite specific developments such as Wireless Underground Sensor Networks, when machinery is "communicated" with sensors under soil [96,112] or underground sensor networks controlling the quality of soils [13,50,81,112]. Further developments in agricultural stage include real-time monitoring of crops growth in field and greenhouses [47,64,94,105], weed control [119], and precise autonomous water or fertilizer management on the field [60,72,93] or frost monitoring [125]. Some authors proposed an overall method for information integration based on a service-oriented architecture (SOA) in agri-food supply chain networks, potentially enhanced with cloud systems, [25,45,116].…”

Section: Current Knowledge On Cps Application In Food Industrymentioning

confidence: 99%

Food Supply Chains as Cyber-Physical Systems: a Path for More Sustainable Personalized Nutrition

2020

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Current food system evolved in a great degree because of the development of processing and food engineering technologies: people learned to bake bread long before the advent of agriculture; salting and smoking supported nomad lifestyles; canning allowed for longer military marches; etc. Food processing technologies went through evolution and significant optimization and currently rely on minor fraction of energy comparing with initial prototypes. Emerging processing technologies (high-pressure, pulsed electric fields, ohmic heating, ultrasound) and novel food systems (cultured biomass, 3-D bioprinting, cyber-physical chains) try to challenge the existing chains by developing potentially more nutritious and sustainable food solutions. However, new food systems rely on low technology readiness levels and estimation of their potential future benefits or drawbacks is a complex task mostly due to the lack of integrated data. The research is aimed for the development of conceptual guidelines of food production system structuring as cyber-physical systems. The study indicates that cyber-physical nature of modern food is a key for the engineering of more nutritious and sustainable paths for novel food systems. Implementation of machine learning methods for the collection, integration, and analysis of data associated with biomass production and processing on different levels from molecular to global, leads to the precise analysis of food systems and estimation of upscaling benefits, as well as possible negative rebound effects associated with societal attitude. Moreover, such data-integrated assessment systems allow transparency of chains, integration of nutritional and environmental properties, and construction of personalized nutrition technologies.

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Section: Current Knowledge On Cps Application In Food Industrymentioning

confidence: 99%

Food Supply Chains as Cyber-Physical Systems: a Path for More Sustainable Personalized Nutrition

2020

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“…While a digital twin is a digital copy of a product or physical system with the intention of performing real-time optimization, a CPS merges computational and physical processes to seamlessly support humans with intelligence when using machines [ 33 ]. CPSs often include digital twins as a base for their decision-making processes [ 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

Henrichs

Noack

Piedrahita

et al. 2021

Sensors

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The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.

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“…The 4.0 technologies applied in the agricultural sector are Big Data [12] , [13] , Cloud Computing [14] , [15] , Cyber-Physical System [16] , [17] , Internet of Things (IoT) [18] , [19] , and Artificial Intelligence (AI) [20] , [21] . The interpretation of information with Machine Learning (ML) techniques allows the development of mathematical models with high estimation accuracy [22] .…”

Section: Introductionmentioning

confidence: 99%

“…Peru has an area of 197,837 hectares which are used for agriculture [4], and is one of the main suppliers of this product in the American market [5], accounting for approximately 3% of the world's organic banana production. As with other organic products, one of the main problems to be faced is the poor management of pests and diseases as-The 4.0 technologies applied in the agricultural sector are Big Data [12,13], Cloud Computing [14,15], Cyber-Physical System [16,17], Internet of Things (IoT) [18,19], and Artificial Intelligence (AI) [20,21]. The interpretation of information with Machine Learning (ML) techniques allows the development of mathematical models with high estimation accuracy [22].…”

Section: Introductionmentioning

confidence: 99%