An ontology model to support the automated design of aquaponic grow beds

Aquaponics is a promising sustainable farming method that combines aquaculture and hydroponics. It allows the growth of crops without soil, pesticides, or fertilizers, and with a minimum amount of water. In aquaponic systems, the design of the growing area is directly linked to the type of crop about to be planted. The type of crop directly determines, for example, the spacing between plants and between channels, which is critical to determine the footprint required and estimate the system productivity. This paper proposes a knowledge modeling approach to support the design of aquaponic systems by automatically determining the required characteristics of the aquaponic system based on crop selection. The knowledge modeling is outlined as an ontology model that formally describes the existent links between the aquaponic grow bed characteristics and its design parameters. This study gives practitioners the capacity to visualize the impact of the desired crop selection on the aquaponic system design, as well as supporting clearer decision-making regarding production facility layout and system design in aquaponic farms

The digitization of agricultural industry – a systematic literature review on agriculture 4.0

Agriculture is considered one of the most important sectors that play a strategic role in ensuring food security. However, with the increasing world's population, agri-food demands are growing — posing the need to switch from traditional agricultural methods to smart agriculture practices, also known as agriculture 4.0. To fully benefit from the potential of agriculture 4.0, it is significant to understand and address the problems and challenges associated with it. This study, therefore, aims to contribute to the development of agriculture 4.0 by investigating the emerging trends of digital technologies in the agricultural industry. For this purpose, a systematic literature review based on Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses is conducted to analyse the scientific literature related to crop farming published in the last decade. After applying the protocol, 148 papers were selected and the extent of digital technologies adoption in agriculture was examined in the context of service type, technology readiness level, and farm type. The results have shown that digital technologies such as autonomous robotic systems, internet of things, and machine learning are significantly explored and open-air farms are frequently considered in research studies (69%), contrary to indoor farms (31%). Moreover, it is observed that most use cases are still in the prototypical phase. Finally, potential roadblocks to the digitization of the agriculture sector were identified and classified at technical and socio-economic levels. This comprehensive review results in providing useful information on the current status of digital technologies in agriculture along with prospective future opportunities

An ontology model to represent aquaponics 4.0 system’s knowledge

Aquaponics, one of the vertical farming methods, is a combination of aquaculture and hydroponics. To enhance the production capabilities of the aquaponics system and maximize crop yield on a commercial level, integration of Industry 4.0 technologies is needed. Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics, internet of things, robotics, cloud computing, and artificial intelligence. The realization of aquaponics 4.0, however, requires an efficient flow and integration of data due to the presence of complex biological processes. A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources. An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing, extracting, and sharing the domains’ knowledge. In the field of agriculture, several ontologies are developed for the soil-based farming methods, but so far, no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model. Therefore, this study proposes a unified ontology model, AquaONT, to represent and store the essential knowledge of an aquaponics 4.0 system. This ontology provides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem. AquaONT is built from indoor vertical farming terminologies and is validated and implemented by considering experimental test cases related to environmental parameters, design configuration, and product quality. The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production, product quality, and facility layout of the aquaponics farm. For future work, a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions

Remote Access of an Autonomous Seed Sowing Robot in a Learning Factory

The supervision of food production systems is instrumental in the advancement of food security. Remote access and control provides unique capabilities to the supervision and operation of such systems, as well as interesting opportunities for students to access learning factory facilities remotely. Thus, the AllFactory at the University of Alberta provides a unique environment that allows for testing of highly robotized food production lines. This paper proposes the use of digital twin models to enable remote access to learning factory’s systems and combines distributed sensors and computer vision to visualize the systems' operational status and motions while also providing a remote learning environment. In this study, a digital twin of a robotic seed sowing system, consisting of a Dobot M1 robotic arms is developed and tested. The robot system aims to pick crop seeds using pneumatic actuators and finally place them correctly in rockwool, while several cameras monitor seed and plant growth. The development of this tool hopes to support the continuous use of learning factories even in complicated situations

Ontology-based Interactive Learning Approach for Transdisciplinary Teaching in Learning Factory

Interactive learning provides a pedagogical approach to acquire skills and develop knowledge regarding transdisciplinary areas, especially in the burgeoning engineering sciences domain. In fact, learning factories have been shown to provide the appropriate environment for transdisciplinary teaching. Exposure to different knowledge domains is desired, but students face difficulties understanding complex foreign concepts over limited periods of time. As an example, the AllFactory at the University of Alberta is a unique facility that encompasses agricultural and biological sciences along with Industry 4.0 technologies. Aquaponics 4.0 mediates the growth of various plant and fish species while using engineering concepts to control the environment and ensure maximum quality control and high throughput. This paper proposes a digital interactive approach to present and teach complex foreign knowledge to students in time-constrained environments. A graphical user interface is designed to provide a step-by-step guide on developing the hydroponic and aquaculture component to investigate Aquaponics 4.0 systems, where all the required knowledge is extracted from a predefined ontology model

Comparison of Energy-use Efficiency for Lettuce Plantation under Nutrient Film Technique and Deep-Water Culture Hydroponic Systems

Energy conservation opportunities in closed plant production systems have been widely discussed, however, a comparison of energy-use efficiency (EUE) for different types of hydroponic systems is lacking. This paper compares the EUE of two different hydroponic systems, namely nutrient film technique (NFT) and deep-water culture (DWC), within an aquaponics facility. The energy is monitored in a controlled environment using artificial lighting and its impact on the growth dynamics of the crops is measured, in this case, on a leafy green crop (Lactuca Sativa L. ‘Little Gem’). Offering better efficiency and reliability, light-emitting diode (LED) irradiation is used with a photosynthetic photon flux (PPF) of 140 µmol·s−1 and a photoperiod of 12-hours. The seeds are then placed in growth chambers, kept at an ambient temperature of 18°C for 21 days. These seedlings are then transplanted in rockwool cubes, followed by placement in NFT or DWC systems in equal numbers. Both systems are illuminated with LED irradiation having a PPF of 200 µmol·s−1. Continuous irradiation with a photoperiod of 16-hours is provided to both systems for 5 weeks. Crop growth parameters, such as leaf count and plant height, are measured in both systems resulting in similar numbers obtained, however, shoot fresh weight, leaf area, and root length are significantly different. Furthermore, the NFT system exhibited an EUE of 31.3 g. kWh−1 and outperformed the DWC system with an EUE of 24.53 g. kWh−1; indicating higher growth and better energy savings associated with NFT systems. These results suggest that NFT systems has a higher potential to offer better energy-use efficiency for producing crops in plant factories and aquaponics facilities

Digital Twinning of Hydroponic Grow Beds in Intelligent Aquaponic Systems

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

Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems

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

Bioresource Nutrient Recycling in the Rice–Wheat Cropping System: Cornerstone of Organic Agriculture

This study evaluated the impact of conventional practices (fertilizer alone) and diverse farming approaches (such as green manuring, farmyard manure application, rice-residue incorporation, residue mulching, residue removal and residue burning) on soil attributes. A total of thirty-five farm sites were selected, with five sites (replications) for each farming approach system, which were used over the past three years in the study farms. Characterization of rice residues of all cultivars, green manure crop (sesbenia: Sesbania sesban) and decomposed farmyard manure samples showed differential behaviours for macronutrients and micronutrients. Continuous application of inorganic fertilizers significantly influenced soil attributes, especially electrical conductivity, nutrient contents, bacterial and fungal population and soil enzymatic attributes. The crop residue treatments favourably influenced the soil parameters over the control. Crop residue incorporation or burning significantly increased soil available potassium, microbial biomass, enzymatic activities and organic carbon when compared with applications of chemical fertilizer alone, while total nitrogen content was increased by residue incorporation. However, green manuring and farmyard manure applications showed inferior responses compared with residue management treatment. It is therefore recommended that bioresources should be managed properly to warrant improvements in soil properties, nutrient recycling and the sustainability for crop productivity, in order to achieve sustainable development goals for climate action

Bioresource Nutrient Recycling in the Rice–Wheat Cropping System: Cornerstone of Organic Agriculture

This study evaluated the impact of conventional practices (fertilizer alone) and diverse farming approaches (such as green manuring, farmyard manure application, rice-residue incorporation, residue mulching, residue removal and residue burning) on soil attributes. A total of thirty-five farm sites were selected, with five sites (replications) for each farming approach system, which were used over the past three years in the study farms. Characterization of rice residues of all cultivars, green manure crop (sesbenia: Sesbania sesban) and decomposed farmyard manure samples showed differential behaviours for macronutrients and micronutrients. Continuous application of inorganic fertilizers significantly influenced soil attributes, especially electrical conductivity, nutrient contents, bacterial and fungal population and soil enzymatic attributes. The crop residue treatments favourably influenced the soil parameters over the control. Crop residue incorporation or burning significantly increased soil available potassium, microbial biomass, enzymatic activities and organic carbon when compared with applications of chemical fertilizer alone, while total nitrogen content was increased by residue incorporation. However, green manuring and farmyard manure applications showed inferior responses compared with residue management treatment. It is therefore recommended that bioresources should be managed properly to warrant improvements in soil properties, nutrient recycling and the sustainability for crop productivity, in order to achieve sustainable development goals for climate action