Maximizing yield in controlled environments is vital for sustainable indoor farming, necessitating an understanding of environmental impacts on plant growth. This study investigates the impact of varying carbon dioxide (CO2) concentrations (400, 800 and 1200 ppm) and light treatments 1) white LED with 28 % red and 25 % blue, 2) white and far-red LED with 23 % red and 29 % blue, 3) amber and blue LED with 24 % red and 35 %blue, all under the same DLI of 12.96 mol m-²day-1 on romaine lettuce (Teton). Lettuce seeds were germinated for 21 days and transplanted into three growth rooms with distinct CO2 levels, relative humidity of 65 % and temperature of 21°C. After 28 days post-transplantation, plant growth parameters such as biomass (fresh and dry mass), height, and leaf characteristics (area, count, chlorophyll, and anthocyanin content) were measured. Findings revealed that using amber and blue light at 1200 ppm CO2 yielded the highest average shoot fresh mass (FM) of 298 g in plants, marking a 30 % increase compared to the same light at 400 ppm CO2. Additionally, an increase of CO2 from 400 ppm to 1200 ppm resulted in a yield boost of 29 % and 18 % for plants under white and far-red, and white light, respectively. These findings suggest a synergistic effect of elevated CO2 and specific light wavelengths on lettuce growth, potentially enhancing photosynthesis. The study provides important insights for horticulture and suggests applications in controlled agriculture, optimizing resources for enhanced crop production.

Greenhouse robots are usually pre-programmed to navigate a predetermined path or move on rails. Once the greenhouse geometry changes, the robots must be re-programmed, or the rails rearranged. To solve this problem, we developed Artificial Intelligence (AI) models capable of identifying the geometry and orientation of the seedbed beneath growing strawberry crops in greenhouses, notwithstanding the age of the crop, presence or absence of mulching sheets, the color of the mulching sheet and background color of the greenhouse floor. Movies of strawberry crops growing in three greenhouses (Greenhouses FWM and TWM with white mulching sheet and Greenhouse TBM with black mulching sheet.) were taken almost daily for about five months after transplanting. These movies were converted to about 2000 images for each of the greenhouses and were separately trained, and evaluated, and a model each (TWM, TBM, and FWM) were developed using the MVTec Halcon 20.11 software. A combination of all the pictures was also used to develop another model (FWMTWMTBM). The four models were tested with about 360 images (120 images from each of the three greenhouses evenly spread over the growing period) randomly taken from seedbeds different from those used for the training. FWMTWMTBM has the highest average Intersection over Union (IoU(0.5)) of 0.935 followed by FWM, TWM, and TBM with average IoUs(0.5) of 0.836, 0.786, and 0.525, respectively. Statistical analyses showed that the precision of FWMTWMTBM was not significantly affected by any of the factors investigated which implies that FWMTWMTBM made accurate predictions notwithstanding the above-mentioned variations.

The optimization of root system architecture (RSA) in crop breeding significantly influences plant vitality, crop yield, and quality. Traditionally, RSA evaluation methods have required root extraction from the soil, a destructive approach that does not preserve the architectural integrity of roots in situ. Alternatives to direct RSA assessment through soil, such as computed tomography (CT) and magnetic resonance imaging (MRI), are prohibitively expensive or lack detail, as is the case with microprobe or rhizobox techniques. A cost-effective strategy involves integrating three-dimensional (3D) computer vision with hydroponic cultivation systems to reconstruct RSA. However, this technique faces considerable challenges due to distortions during the data collection phase, primarily caused by the refraction of the container and nutrient solution. These distortions prevent the reconstruction of high-precision 3D point clouds using traditional Structure from Motion (SfM) algorithms. Fortunately, recent advances in the 3D reconstruction, such as Neural Radiance Fields (NeRF) and 3D Gaussian splatting, have shown promise in rendering high-fidelity, distortion-resistant 3D models. Our methodology is structured as follows. First, we acquire multi-view stereo RGB images of roots within a hydroponic system. Second, we employ multiple latest 3D reconstruction pipelines, including Colmap, NeRF, and 3D Gaussian Splatting, to obtain 3D reconstruction models of roots in the hydroponic system. Third, we perform cross-validation with open-source 3D reconstruction pipelines to ascertain the superiority of a particular method. This study facilitates the identification of the most effective 3D root reconstruction method for use in hydroponic settings, thereby enabling detailed subsequent analyses of various root traits.

A novel remote beehive monitoring technology employing an interdigital capacitor array is presented. This capacitor array consists of 12 interdigitated capacitors on a standard beehive frame, with the goal of monitoring beehive contents and activity through changes in electrical permittivity. Six of these sensor frames are used in the beehive, split between a single honey super and a single brood box in a Langstroth hive. The sensor boards of each box interface with a microcontroller for data capture. Temperature and humidity sensors, and light sensors were included in the system, and periodically a weight sensor was used, as they are well established in the smart-hive literature. Data was collected over three years, during which events such as colony collapse, nectar flow, and swarming events took place. Compared to the common method of monitoring hive contents, a weight sensor, this capacitive system is shown to monitor hive contents and their distribution throughout the hive—at a greatly reduced cost. From continuous monitoring at 30 minute intervals, the time series data can be decomposed to a trend and periodic components. The trend component readily captures the beginning of nectar flow, colony collapse, and the early detection and aftermath of a swarm. The periodic component shows the bees leaving and returning each day, which allows for qualitative estimation of forager numbers, and detection of localised clustering.

Ultraviolet (UV) C radiation finds extensive application as a non-chemical disinfection method in the medical field, food processing and water treatment; however, its germicidal effectiveness depends on factors such as radiation wavelength, radiant exposure, microbial physiology, biological matrices, and surface characteristics. This research aimed to develop and evaluate the control and radiation parameters of an automated intelligent UV disinfection system operating in both pulsed and continuous modes, that respects threshold limit values of radiant exposure in human-occupied spaces. For this purpose, a user-friendly interface was constructed to include a 222 nm KrCl excimer lamp and 280 nm UV-LEDs. Other design parameters included programmable switching between continuous and pulsed modalities, UV sensors to monitor and record UV irradiance, a CCD camera to observe and track notable alterations throughout the experiment, a distance sensor to measure the absorbed UV dose by experimental media, motion detection, temperature, relative humidity, pressure drop and energy consumption of system in a real-time manner. Data sampling and process control were conducted using MyDAQ through LabVIEW® software integrated with Arduino software. UV light mapping was performed at different distances from the radiation sources and assessed for optimal surface decontamination within an 8-hour period. Other potential solutions to enhance germicidal effectiveness were explored, including overpowering the radiation sources to increase irradiance. Future experimentation will comprise testing on contaminated surfaces and popular greenhouse crops to determine microbial log reduction. Anticipated outcomes include a safe and user-friendly pathogen control tool with potential applications in room surface disinfection and controlled environment agriculture.

Controlled Environment Agriculture has emerged as a fundamental pillar of the primary-level food production industry. Getting desirable results in controlled environments depends on efficiently manipulating and managing the factors that directly or indirectly interact with the plants. Thus, this study aims to determine the effect of different LED wavelengths on Oats and Barley while also determining the performance of a novel hydroponic substrate, permeable concrete in supporting plant growth in greenhouse conditions. This research investigated the plant-light response of 3 LED light treatments; Narrow Amber+ 430nm, 5000k + Wide Amber and Narrow Amber +430nm +485nm and additional treatment under natural sunlight Plant-light response and a comparison between the hydroponic substrates was measured based on data acquired on plant growth rate over 45 days in the hydroponic setup. In addition, data on fresh and dry biomass production was investigated at the time of harvest. The preliminary results showed 5000k + Wide Amber showed 25% better results than both single and double narrow amber standing close to each other, quantitatively followed by the natural light treatments in terms of increased plant growth rate. The future implications of this research will allow to enumerate the potential edible yield under the same conditions that will have commercial significance in selecting the optimum artificial light sources under controlled environments.

Global warming has escalated energy consumption in cooling, emphasizing the pivotal role of Heating, Ventilating, and Air-Conditioning (HVAC) systems in providing comfort and indoor air quality. However, conventional HVAC systems struggle with excess moisture during high-humidity periods. This study introduces a novel dehumidification approach inspired by natural fog formation. Employing a vortex cooling gun and cold plate, this method can extract moisture from saturated vapor, transforming it into fog or ice particles. By manipulating the temperature, relative humidity, and pressure, we achieved a remarkable temperature reduction, concurrently reducing indoor relative humidity from 100% to 19%. This innovation offers energy-efficient humidity control for HVAC systems, addressing climate change challenges.