Low-Cost Underwater Camera: Design and Development Dadios, Elmer P.; Almero, Vincent Jan; II, Ronnie S. Concepcion ...
Journal of advanced computational intelligence and intelligent informatics,
09/2022, Letnik:
26, Številka:
5
Journal Article
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The understanding of vision-based data acquisition and processing aids in developing predictive frameworks and decision support systems for efficient aquaculture monitoring and management. However, ...this emerging field is confronted by a lack of high-quality underwater visual data, whether from public or local setups and high cost of development. In this regard, an underwater camera that captures underwater images from an inland freshwater aquaculture setup was proposed. The components of the underwater camera system are primarily based on Raspberry Pi, an open-source computing platform. The underwater camera continuously provides a real-time video streaming link of underwater scenes, and the local processor periodically acquires and stores data from this link in the form of images. These data are stored locally and remotely. Based on the results of the developed low-cost underwater camera, it captures and differentiate fish region to its background before and after flushing as influenced by turbidity. Hence, the developed camera can be used for both aquarium and inland aquaculture pond setup for fish monitoring.
Grapes are prone to
Pseudocercospora vitis
fungus which causes
Isariopsis
leaf speck disease to the crop’s leaves, flower, and most importantly the fruit. Typical manual inspection of vineyard ...farmers is normally ineffective, destructive, and laborious. To address this, the use of integrated computer vision, machine learning, and computational intelligence techniques were realized to sort out healthy grape leaf image from a fungus-specked leaf image and to estimate the specked area percentage (SAP). A dataset made up of 343 normally healthy and 200 fungus-specked grape leaf images were initially pre-processed and segmented via graph cut prior to feature extraction and selection. Significant features were identified using classification tree (CTree). A multigene genetic programming tool called GPTIPSv2 was utilized to generate the fitness function needed for the optimization process done via genetic algorithm (GA). An optimal hidden neuron counts of 110, 50, and 10 were selected for a three-layered GA-optimized recurrent neural network (GA-RNN). Linear discriminant analysis (LDA) topped other disease recognition models with an accuracy of 99.99%. The developed GA-RNN model outperformed Gaussian process regression (GPR), regression tree (RTree), regression support vector machine (RSVM), and linear regression (RLinear) in performing leaf specked area estimation with an
R
2
value of 0.822. The developed CTree-LDA
2
-GA-RNN
2
hybrid model has been proven to be the most viable model for this task.
The knowledge of induction generator models and their parameters has gained great importance in recent years. Induction generators have been widely used in several applications, including renewable ...energy systems, because of their simple construction and easy operation. A successful parameter's estimation of induction generators strongly depends on the availability of a good initial parameter guess. When it is not available, the estimation process could take plenty of time to converge or even to diverge. This paper proposes a hybrid method that estimates parameters of induction generator transient models from disturbance measurements through a hybrid algorithm based on trajectory sensitivity and mean-variance mapping optimization. The method is robust regarding initial parameter guesses, requires no disconnection of the generator from the grid, and uses measurements commonly available in practice, such as generator terminal voltage and current. The system modeling for estimation purposes is based on a squirrel-cage induction generator, represented by a third-order model, connected to both a grid and a static load. The method was tested with actual measurements obtained from a small-sized power system designed in the laboratory. The results show correct estimates were successfully achieved and the model can represent the dynamic response of the system accurately.
Spray drying is a rapid, continuous, cost-effective, reproducible, and scalable process for reducing the moisture content of a fluid material into a solid powder. To improve this process in juice ...powder production, automation can be applied to increase efficiency and productivity. Hence, fuzzy logic is used in this study as a control system in the spray-drying process of concentrated liquid bignay juice into juice powder, where the inlet temperature and carrier agent concentrations affecting the properties of the juice powder, such as moisture content and product yield, are considered. The proposed fuzzy system can provide feedback to the control variables, inlet temperature, and carrier agent concentration based on the moisture content and product yield of the juice powder.
Poultry, like quails, is sensitive to stressful environments. Too much stress can adversely affect birds’ health, causing meat quality, egg production, and reproduction to degrade. Posture and ...behavioral activities can be indicators of poultry wellness and health condition. Animal welfare is one of the aims of precision livestock farming. Computer vision, with its real-time, non-invasive, and accurate monitoring capability, and its ability to obtain a myriad of information, is best for livestock monitoring. This paper introduces a quail detection mechanism based on computer vision and deep learning using YOLOv5 and Detectron2 (Faster R-CNN) models. An RGB camera installed 3 ft above the quail cages was used for video recording. The annotation was done in MATLAB video labeler using the temporal interpolator algorithm. 898 ground truth images were extracted from the annotated videos. Augmentation of images by change of orientation, noise addition, manipulating hue, saturation, and brightness was performed in Roboflow. Training, validation, and testing of the models were done in Google Colab. The YOLOv5 and Detectron2 reached average precision (AP) of 85.07 and 67.15, respectively. Both models performed satisfactorily in detecting quails in different backgrounds and lighting conditions.
Genetic programming (GP) is an evolutionary algorithm used to produce high-quality solutions to various problems. It has seen few claims in circuitry and the development of antenna designs. The ...application of GP in the model of embedded digital systems on multi-channel antenna arrays of subsurface imaging equipment has not yet been investigated. This study focuses on designing and developing a digital multimeter embedded with a multigene genetic programming (MGGP) model for multi-array transmitter antenna used for subsurface imaging operating at a low frequency of 3.5 kHz to 18.5 kHz using Arduino microcontroller for prototyping. The electrical outputs of a transmitter antenna system employed in a subsurface imaging device require live measurement and monitoring during operation for data logging purposes. The amount of transmitted voltage, produced current, and operating frequency are significant parameters for mapping the underground resistivity, thus the produced GP models are functions of the three parameters. GP fitness function was determined through MATLAB software. The output current signal from the transmitter were imitated in Proteus simulation software using a current source in the designed current measuring circuit. This produced linear and nonlinear relationships of the electrical outputs where GP modeling was beneficially applied. The application of GP in with the microcontroller provided an accurate reading of frequency, current and voltage produced by the multi-array transmitter antenna. These measurements were displayed using LM016L LCD display. Moreover, this embedded digital multimeter on transmitter antenna avoids utilizing costly high voltage measuring devices.
Recommendations for control of high blood pressure (BP) emphasize lifestyle modification, including weight loss, reduced sodium intake, increased physical activity, and limited alcohol consumption. ...The Dietary Approaches to Stop Hypertension (DASH) dietary pattern also lowers BP. The PREMIER randomized trial tested multicomponent lifestyle interventions on BP in demographic and clinical subgroups. Participants with above-optimal BP through stage 1 hypertension were randomized to an Advice Only group or one of two behavioural interventions that implement established recommendations (Est) or established recommendations plus DASH diet (Est plus DASH). The primary outcome was change in systolic BP at 6 months. The study population was 810 individuals with an average age of 50 years, 62% women, 34% African American (AA), 95% overweight/obese, and 38% hypertensive. Participants in all the three groups made lifestyle changes. Mean net reductions in systolic (S) BP in the Est intervention were 1.2 mmHg in AA women, 6.0 in AA men, 4.5 in non-AA women, and 4.2 in non-AA men. The mean effects of the Est Plus DASH intervention were 2.1, 4.6, 4.2, and 5.7 mmHg in the four race-sex subgroups, respectively. BP changes were consistently greater in hypertensives than in nonhypertensives, although interaction tests were nonsignificant. The Est intervention caused statistically significant BP reductions in individuals over and under age 50. The Est Plus DASH intervention lowered BP in both age groups, and significantly more so in older individuals. In conclusion, diverse groups of people can adopt multiple lifestyle changes that can lead to improved BP control and reduced CVD risk.
Unlike a media-filled aquaponic system, the nutrient film technique (NFT) and deep water culture (DWC) require the installation of an external biofilter to provide sufficient area for nitrifying ...bacteria colonization, which is essential for the conversion of toxic ammonia from fish waste into nitrate that is easily assimilated by plants. Given the importance of biofilters, it is imperative to properly design this tank to effectively support the nitrification process. Several factors need to be considered for the biofilter design. Thus, an optimization algorithm can be used to obtain combinations of the design parameters. The genetic algorithm (GA) is a heuristic solution search or optimization technique based on the Darwinian principle of genetic selection. The main goal of this study was to obtain the optimal biofilter size for a given fishpond volume and the amount of ammonia to be treated. The conversion coefficient in the Michaelis–Menten equation was used as the fitness function in this study. The parameters optimized using GA include the hydraulic loading rate, height of the biofilter, and predicted ammonia concentration. For the given assumption of a 60 kg feed introduced to the system and a 1500 L fishpond, the hydraulic loading rate, biofilter height, and final concentration of ammonia were 0.17437 m, 0.58585 m, and 0.01026 ppm, respectively. Using the values obtained from running the GA, the optimum biofilter volume for the system was 0.4608 m
3
, whereas the water flow rate was 0.03 L/min. For recommendations, multiple objective GAs can be used to add cost-related variables in the optimization because they have not yet been considered in the computation.
Seed varieties are often differentiated via the manual and subjective classification of their external textural, spectral, and morphological biosignatures. This traditional method of manually ...inspecting seeds is inefficient and unreliable for seed phenotyping. The application of computer vision is an ideal solution allied with computational intelligence. This study used
Lactuca sativa
seed variants, which are commercially known as grand rapid, Chinese loose-leaf, and iceberg (which serves as noise data for extended model evaluation), in determining their corresponding classifications based on the extended morphological phenes using computational intelligence. Red-green-blue (RGB) imaging was employed for individual kernels. Extended morphological phenes, that is, solidity, roundness, compactness, and shape factors, were computed based on seed architectural traits and used as predictors to discriminate among the three cultivars. The suitability of ANFIS, NB, and CT was explored using a limited dataset. A mean accuracy of 100% was manifested in ANFIS; thus, it was proved to be the most reliable model.
AIMS: To investigate the efficacy and mode of action of the fatty acid–based product Foodcoat®(FC) against Botrytis cinerea. METHODS AND RESULTS: In vitro, in vivo and field experiments were carried ...out to investigate the effect of different concentrations of FC on B. cinerea germination and infection of grape leaves and berries, using three selected isolates and comparing results with those achieved by the commercialized product Protectorᴴᴹᴸ(PRT). Furthermore, the effect of field applications of FC on the grape berry microbiota was investigated. FC reduced B. cinerea germination and grape berry severity by up to 54 and 96%, respectively, compared with the untreated controls. CONCLUSIONS: Foodcoat demonstrated efficacy that was equal or greater than the registered product, PRT. A multiple mode of action was hypothesized for FC suppression of B. cinerea, including: inhibition of germination and germ tube alteration, protection of host green tissues and enhancement of the natural yeast populations on the berry surface. SIGNIFICANCE AND IMPACT OF THE STUDY: The efficacy of both products has been quantified and their modes of action described, suggesting them for field applications against B. cinerea, alone or in combined strategies. This is also the first report of a fatty acid–based product stimulating natural yeast populations on grape berries.
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