Nutrient monitoring in Micro Indoor Smart Hydroponics (MISH) relies on measuring electrical conductivity or total dissolved solids to determine the amount of nutrients in a hydroponic solution. Neither method can distinguish concentrations of individual nutrients. This study presents the development and testing of a novel spectroscopic sensor system to monitor nitrogen changes in nutrient solutions for MISH systems. The design phase determined that using an inexpensive AS7265x Internet of Thing (IoT) sensor in a transflective spectroscopic application could effectively detect small fluctuations in nitrogen concentraation. Next, a novel transflective sensor apparatus was designed and constructed for use in a MISH system experiment, growing lettuce over 30 days. Two solution tanks of different sizes, 80 L and 40 L, were used in the deployment of the system. Samples from each tank were analyzed for nitrogen concentration in a laboratory, and multilinear regression was used to predict the nitrogen concentrations using the AS7265x 18 spectral channels recorded in the sensor system. Significant results were found for both tanks with an R2 of 0.904 and 0.911 for the 80 and 40 L tanks, respectively. However, while the use of all wavelengths produced an accurate model, none of the individual wavelengths were indicative on their own. These findings indicate that the novel system presented in this study successfully and accurately monitors changes in nitrogen concentrations for MISH systems, using low cost IoT sensors.

Environmental changes and the reduction in arable land have led to food security concerns around the world, particularly in urban settings. Hydroponic soilless growing methods deliver plant nutrients using water, conserving resources and can be constructed nearly anywhere. Hydroponic systems have several complex attributes that need to be managed, and this can be daunting for the layperson. Micro Indoor Smart Hydroponics (MISH) leverage Internet of Things (IoT) technology to manage the complexities of hydroponic techniques, for growing food at home for everyday citizens. Two prohibitive costs in the advancement of MISH systems are power consumption and equipment expense. Reducing cost through harvesting ambient light can potentially reduce power consumption but must be done accurately to sustain sufficient plant yields. Photosynthetic Active Radiation (PAR) meters are commercially used to measure only the light spectrum that plants use, but are expensive. This study presents Adaptalight, a MISH system that harvests ambient light using an inexpensive AS7265x IoT sensor to measure PAR. The system is built on commonly found IoT technology and a well-established architecture for MISH systems. Adpatalight was deployed in a real-world application in the living space of an apartment and experiments were carried out accordingly. A two-phase experiment was conducted over three months, each phase lasting 21 days. Phase one measured the IoT sensor’s capability to accurately measure PAR. Phase two measured the ability of the system to harvest ambient PAR light and produce sufficient yields, using the calibrated IoT sensor from phase one. The results showed that the Adaptalight system was successful in saving a significant amount of power, harvesting ambient PAR light and producing yields with no significant differences from the control. The amount of power savings would be potentially greater in a location with more ambient light. Additionally, the findings show that, when calibrated, the AS7265x sensor is well suited to accurately measure PAR light in MISH systems.

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The purpose of this study was the development of an expert system that is able to recommend types of plants for the removal of toxic substances in a given artificial ecosystem. The domain was restricted to office environments within the United Arab Emirates (UAE). A literature review revealed a significant gap in research related to systems that help select plants based on a given environment. This eventually led to the creation of the Plant Recommender Expert System (PRES). PRES utilizes a set of inputs (for example expected humidity, average temperature, light quality, etc.). The system will then recommend the type(s) of plants that should be purchased to suit the given environment conditions. Horticultural experts usually give recommendations for these types of problem domains but such an expert may not always be available, and so the concept here is to encode the expertise knowledge in an intelligent system to ensure uninterrupted availability of the expert knowledge. The system was evaluated using several case studies with known outcomes. The PRES suggested plants for environments that were described via a set of inputs in these trials. The initial phase, being a prototype of PRES was created with the aim of helping non-expert users leverage the natural airdetoxification properties of plants. While in its current configuration the system is not capable of learning, it can at a later stage be integrated with other AI methods such as the use of decision trees to create further rules or data driven approaches such as the use of Neural Networks for the classification of plants relevant to a given domain. However, the latter approach will first necessitate the creation of a relevant data set. CCS Concepts • Information systems →Expert systems • Computing methodologies → Knowledge representation and reasoning.