The use of automation, Internet-of-Things (IoT), and smart technologies is being rapidly introduced into the development of agriculture. Technologies such as sensing, remote monitoring, and predictive tools have been used with the purpose of enhancing agriculture processes, aquaponics among them, and improving the quality of the products. Digital twinning enables the testing and implementing of improvements in the physical component through the implementation of computational tools in a ‘twin’ virtual environment. This paper presents a framework for the development of a digital twin for an aquaponic system. This framework is validated by developing a digital twin for the grow beds of an aquaponics system for real-time monitoring parameters, namely pH, electroconductivity, water temperature, relative humidity, air temperature, and light intensity, and supports the use of artificial intelligent techniques to, for example, predict the growth rate and fresh weight of the growing crops. The digital twin presented is based on IoT technology, databases, a centralized control of the system, and a virtual interface that allows users to have feedback control of the system while visualizing the state of the aquaponic system in real time.

With significant advancement in information technologies due to Industry 4.0, Digital Twin (DT) has gained increasing attention as it offers an enabling tool to realize digitally-driven and cloud-enabled production systems. The idea of DT involves the concrete realization of mirroring the physical entities in the virtual world. Given the nonlinear dynamics and uncertainty involved in production systems, the proper design, and adaptability of a DT model remain a challenge. To address this issue, the given study proposes a methodology to develop the DT model for production systems, mainly focusing on robot-based production lines. The implementation and validation of the model are carried through a case study within a Learning Factory that involves data extraction from a physical pick and place robotic production line and using it as an input for the virtual entity to determine and analyze lean process parameters such as cycle time and total lead time. The proposed study also facilitates Lean Industry 4.0 concepts. The gathered results show that DT technology not only enables remote monitoring but also improves the performance of the production system.

Chlorosis, or leaf yellowing, in crops is one of the quality issues that primarily occurs due to interference in the production of chlorophyll contents. The primary contributors to inadequate chlorophyll levels are abiotic stresses, such as inadequate environmental conditions (temperature, illumination, humidity, etc.), improper nutrient supply, and poor water quality. Various techniques have been developed over the years to identify leaf chlorosis and assess the quality of crops, including visual inspection, chemical analyses, and hyperspectral imaging. However, these techniques are expensive, time-consuming, or require special skills and precise equipment. Recently, computer vision techniques have been implemented in the agriculture field to determine the quality of crops. Computer vision models are accurate, fast, and non-destructive, but they require a lot of data to achieve high performance. In this study, an image processing-based solution is proposed to solve these problems and provide an easier, cheaper, and faster approach for identifying the chlorosis in lettuce crops grown in an aquaponics facility based on their sensory property, foliage color. The ‘HSV space segmentation’ technique is used to segment the lettuce crop images and extract red (R), green (G), and blue (B) channel values. The mean values of the RGB channels are computed, and a color distance model is used to determine the distance between the computed values and threshold values. A binary indicator is defined, which serves as the crop quality indicator associated with foliage color. The model’s performance is evaluated, achieving an accuracy of 95%. The final model is integrated with the ontology model through a cloud-based application that contains knowledge related to abiotic stresses and causes responsible for lettuce foliage chlorosis. This knowledge can be automatically extracted and used to take precautionary measures in a timely manner. The proposed application finds its significance as a decision support system that can automate crop quality monitoring in an aquaponics farm and assist agricultural practitioners in decision-making processes regarding crop stress management.

Aquaponics is a sustainable fish and plant production process in an indoor ecosystem. Recent development in aquaponics has led to the development of new automated technologies which can serve in high automation, efficiency, and waste minimization. Zero-defect production systems are key to high quality in a production environment where cyber-physical systems, data acquisition, and decision-making are paramount. This paper presents a novel framework based on a cyber-physical systems (CPS) architecture that provides data acquisition, data analytics, and data storage that supports defect damage detection and identification parameters optimizations to create an autonomous aquaponics production system. The proposed method is implemented and validated in the context of an indoor aquaponics environment with a full factory setup created in aquaponics 4.0 learning factory (AllFactory).