Stereo Vision 3D Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video

3D multiple fish tracking has gained a significant growing research interest to quantify fish behavior. However, most tracking techniques have used a high frame rate that is currently not viable for real-time tracking applications. This study discusses multiple fish tracking techniques using low frame rate sampling of stereo video clips. The fish are tagged and tracked based on the absolute error of predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, the linear regression and machine learning algorithms intended for nonlinear systems, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), symbolic regression, and Gaussian Process Regression (GPR), were investigated. Results have shown that in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, i.e., 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms

Stimulation of Static Electric Field and Exposure Time on Germination and Stem Tissues of Hybrid Philippine Zea mays Genotypes

The Philippines has a tropical and maritime climate that inhibits the agricultural lands from continuous production. Because industrial crops are sensitive to electrotropism, stimulating them may break the germination dormancy and improve the growth and quality but the amount of effective electric field depends on each genotype. In this study, three hybrid Philippine maize genotypes namely, NSIC CN 282, IPB VAR6, and PSB CN 97-97, were cultivated in three replicates in an electroculture system with 0.4 V/cm electric fields. Four treatments were employed: 5 minutes daily (T5), 10 minutes (T10), 15 minutes (T15), and control. Germination rate of each genotype was modeled using 5-gene genetic programming. To verify the impact of the electric field to plant tissue, morpho-anatomical microscopy was performed. Longer exposure time to a static electric field (T10-T15) resulted in more basal roots, longer and heavier root and shoot systems. T15-treated seedlings exhibited an advanced proliferation in stem and parenchymal tissue thickness. Also, T15-treated seedlings were observed to have more dominant and thicker xylem and phloem vessels that biologically allow the ease of water transport and sugar mobilization from leaves to other parts of the plant system, thus, accelerating growth. NSIC CN 282 and PSB CN 97-92 are more sensitive to electric field stimulation than the IPB VAR6. Based on the findings, the germination is not completely and directly relational to the growth after two weeks of cultivation but having high germination score revealed to be a relatively good determinant of root and shoot quality

Image fusion of multidirectional wavelet transforms for image denoising

Image denoising in the wavelet domain has been attractive to researchers in the past decade due to its suitable properties which lead to a smooth denoised image. However, limitations in edge representation have been found particularly with diagonal edges which may be destroyed in the denoising process. Direction-sensitive variants of the transform have been proposed by various researchers but they are often computationally expensive and difficult to implement in an actual system. This work presents a direction-sensitive technique of denoising based on a reversible rotation process combined with a simple discrete wavelet transform and image fusion methods. Test results on the said methods show significant gains over the traditional wavelet denoising process. Additionally, the simple and non-adaptive nature of the process makes it attractive for software and hardware implementation

Determination of aquaponic water macronutrient concentrations based on lactuca sativa leaf photosynthetic signatures using hybrid gravitational search and recurrent neural network

Crop quality depends dominantly on the nutrients present in its growth media. For precision farming, fertigation is a challenge, especially when dealing with economical and efficiency factors. In this study, the aquaponic pond water macronutrient prediction model (wNPK) was developed based on leaf photosynthetic signature predictors. Aquaphotomics was preliminarily used for correlating physical limnological properties with nitrate, phosphate, potassium concentrations, and the leaf signatures. Using a digital camera, 18 spectro-textural-morphological features were extracted. Neighborhood component analysis (NCA) and ReliefF algorithms selected the spectral components blue, a*, and red minus luma as the most significant as supported by principal component analysis, resulting in low computational cost. A Gravitational Search Algorithm (GSA) was employed to optimize the recurrent neural network (RNN) architecture resulting in higher sensitivity. The hybrid NCA-ReliefF-GSA-RNN (wNPK) predicted NPK with 93.61, 84.03, and 91.39 % accuracy, respectively, besting out other configured feature-based machine learning models. Using wNPK, it was confirmed that potassium helped in accelerating seed germination and nitrogen in promoting chlorophyll intensification, especially on the 6th week after sowing. Phosphate and potassium were the energy and health elements that were consumed in a larger amount at the end of the head development stage. wNPK rules out that macronutrient concentration have a direct resemblance to crop leaf signatures; thus, a leaf is a good indicator of the water quality. The results pointed out that the use of a single camera to measure both water macronutrient concentrations and crop signature at the same time is an innovative, efficient, and economical approach for precision farming.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Image compression using adaptive discrete wavelet transforms on image seams

The discrete wavelet transform (DWT) is a flexible tool in signal processing. Its use for image processing and particularly the matter of lossless and lossy compression hive been recognized in various studies. However, the ability of DWT to effectively represent image data is limited to smooth image regions. Discontinuities in the form of edges are expensive to code. We investigate the use of an adaptive transform to reduce the occurrences of large wavelet coefficients. A direction selection algorithm is introduced to subdivide the image into discrete blocks with each block assigned to an arbitrary direction. Transforms occurring between blocks are adapted to the direction of the concerned pixels to prevent boundary distortions. To encode the coefficients to a bitstream, a data clustering variant of SPIT is also introduced with the intention of lowering quantization errors for low bitrates. Preliminary test results based on PSNR and SSIM comparisons show a comparable performance to JPEG2000 even without the use of an entropy encoder

Adaptive fertigation system using hybrid vision-based lettuce phenotyping and fuzzy logic valve controller towards sustainable aquaponics

Sustainability is a major challenge in any plant factory, particularly those involving precision agriculture. In this study, an adaptive fertigation system in a three-tier nutrient film technique aquaponic system was developed using a non-destructive vision-based lettuce phenotype (VIPHLET) model integrated with an 18-rule Mamdani fuzzy inference system for nutrient valve control. Four lettuce phenes, that is, fresh weight, chlorophylls a and b, and vitamin C concentrations as outputted by the genetic programming-based VIPHLET model were optimized for each growth stage by injecting NPK nutrients into the mixing tank, as determined based on leaf canopy signatures. This novel adaptive fertigation system resulted in higher nutrient use efficiency (99.678%) and lower chemical waste emission (14.108 mg L-1) than that by manual fertigation (92.468%, 178.88 mg L-1). Overall, it can improve agricultural malpractices in relation to sustainable agriculture. © Fuji Technology Press Ltd.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Philippine license plate character recognition using faster R-CNN with inceptionV2

This research proposes a method for automatic license plate recognition (ALPR) using the Faster R-CNN with InceptionV2 feature extractor that works in the Philippines. While there exist character recognition systems, there still remains difficulty in recognition due to different variations of Philippine license plates. By training a deep neural network in the extraction of the features in images of the different types of Philippine license plates - 1981, 2003, 2014, and others - our proposed multi-class detection system can recognize the alphanumeric characters in the license plate images. The system was tested on actual traffic images in the Philippines that contains different types of license plates, and achieved the detection rate of 90.011%, recognition rate of 93.21% and an overall accuracy of 83.895%. © 2019 IEEE

Environmental impact prediction of microalgae to biofuels chains using artificial intelligence: A life cycle perspective

Biofuels derived from microalgae is an emerging technology that can supply fuel demand and alleviate greenhouse gas emissions. However, exclusively producing biofuels from microalgae remains to be commercially unsustainable because of its high investment and operating costs. A promising opportunity to address this are algal bio-refineries. Nonetheless, there is still a need to verify the environmental sustainability of this system along its entire process chain, from raw material acquisition to end-of-life. This study utilizes a life-cycle perspective approach to assess the sustainability of the algal bio-refinery and developed environmental impact prediction model using artificial intelligence, particularly adaptive neuro fuzzy inference system. Results will indicate the environmental impacts of a bio-refinery system identifying its major hotspots on different environmental impact categories. Results show that in the investigated proposed algal bio-refinery, the transesterification process had a huge contribution on the overall environmental impact having over 51.5 % of the total weight. In addition, ANFIS results showed the correlation of input parameters with respect to the environmental impact of the system. The model also indicated that there is a perfect correlation between the two parameters. The model and its accuracy should be further validated with the use of real data. © 2020 Institute of Physics Publishing. All rights reserved

Optimization of vehicle classification model using genetic algorithm

This paper focuses on classifying vehicle types into car, van, motorcycle, bus, light truck, multi-axle truck and determine its class based on the Philippine Toll Regulatory Board\u27s vehicle classification. This study utilized DEvol, an open-source tool that uses genetic algorithm for evolving number of filters and nodes, optimizer, activation, dropout rate. The model attained the best accuracy with 78.53% using 9000 images from MIO-TCD dataset. © 2019 IEEE