Aquaponics systems allow simultaneous growth between vegetables and fish with the use of aquatic products. This process is due to the presence of beneficial microbial communities such as nitrifying bacteria. Numerous water quality problems can be addressed by improving the conversion efficiency of biological filtration systems. Maintaining parameters including increased ammonia and nitrite concentration, organic matter accumulation, and decreased levels of dissolved oxygen are imperative to maximize system productivity. The pilot-scale model of the aquaponics system features two independent systems: the control (with biofilter), and the experimental (without biofilter). The media in the biofilter is as follows: an aeration system (for oxygen supply), and a centralized sensory chamber (to facilitate automatic monitoring of pH, temperature, dissolved oxygen, and turbidity). This study assessed the performance of an aquaponic setup with a biofiltration system in terms of reducing the concentration of dissolved solid particles in the water. The correlations of ammonia, nitrate, and nitrate concentrations with turbidity were explored. The turbidity was monitored using a turbidity sensor. To test ammonia, nitrite, and nitrate, solutions were dropped into water samples, whose developed color was compared to a color chart. The system must operate in conditions that cater to the collective growth and development of the fish, plants, and nitrifying bacteria. The plant crop utilized in this study is mint (Mentha spicata), while the fish used was the Red Tilapia (Oreochroomis aureus x Oreochromis mossambicus). Ammonia levels were found to be substantially connected with nitrate levels, and both parameters were shown to be inversely correlated with turbidity. Turbidity in water may be caused by active microorganisms or algae that may metabolize ammonia and nitrate.

The limited physical constraints of nanosatellites due to their size, hinders their ability to transmit large amounts of image data. Because of this, the use of machine learning methods to filter data onboard has become more prominent to increase the bandwidth efficiency of these devices. By having an AI-based classification system for the images, the bandwidth necessary to transmit all these images and the tradeoff when it comes to storage, can potentially be offloaded through having a system which generates metadata that can indicate the data samples which offer the most usability, thus freeing up more space and bandwidth for these more important samples. This study explores the task of land cover classification, by utilizing one of the more prominent image segmentation models, U-Net. The model is implemented and evaluated using Pytorch using the DeepGlobe 2018 land cover classification dataset, achieving an average class IoU score of 0.68. This study seeks to support the viability of such a solution and is intended to support any future work which seeks to implement a fully automated data prefiltering system for satellite imagery.

The development of machine learning methods for onboard satellite processing is important in order to facilitate the filtering of collected data samples to maximize the use of the device's limited resources. Land cover classification can be used to focus the collected data on certain terrain types by utilizing classification methods to determine the class probabilities of individual pixels in a collected satellite image. The importance of the accuracy of the segmentation model used for such a task is important in order to avoid the trashing of data samples that offer significant information and the prioritization of data samples which offer less in terms of usable information, which in the case of land cover classification is determined by which terrain features may be prioritized over others. This study focuses on the U-Net segmentation architecture and performs an experimental study on the effects on two aspects on the training of a segmentation model for increased performance. This includes the division of the images in the dataset into smaller patches and the replacement of the CNN encoder of the segmentation architecture. The changes made to the baseline model introduced an increase in the IoU score from 0.68 to 0.7273.

The advent of the fourth industrial revolution (4IR) offers promising improvements in operational efficiency and profitability for various industries, and can be a key component to leapfrog the many sectors of manufacturing in the Philippines. This will necessitate the reskilling and upskilling of Filipino automation engineers and instrumentation technicians to commission and maintain smart technologies for production facilities. The further expansion of the country's manufacturing capacities presents an opportunity to locally develop products and solutions for process automation. This includes sensor technologies and applications that can boost the Filipino manufacturers' capabilities through locally-sourced automation components. Various sensor technologies and applications can be explored for further improvement through research & development. Through technology foresight, this study looks into the potential of Filipino technology firms to develop sensors that are locally designed and assembled, addressing the needs of growing industries. The use of scenario-building approach allows for the identification and ranking of the key predictable drivers based on the insights of industry professionals. Opportunities and risks are evaluated based on future possible scenarios.

Artificial Neural Networks (ANNs) are increasingly recognized as valuable tools for crop quality parameter measurement. This study investigates the ANNs effectiveness in the predictive measurement of the Chlorophyll-a levels of iceberg lettuce (Lactuca sativa var. capitata). This involved using ANNs to link the dataset of extracted RGB and HSV values with the Chlorophyll-a levels retrieved with UV - VIS spectroscopy. For the prediction model, the RGB and HSV values were used as the 6 input predictor values, while the Chlorophyll-a level was used as the 1 output response value. The ANN s were trained on this dataset using the Levenberg-Marquardt algorithm, where the training data comprised 70% of the dataset, the validation data 20% of the dataset, and the test data 10% of the dataset with a layer size of 15. The ANN model demonstrated a strong correlation between the predicted and target outputs, with an accuracy of 98.02% for the test data. This suggests that ANNs can be employed for an accurate and non-invasive monitoring of parameters in iceberg lettuce. The findings also open possibilities for other crops in the Philippines' agricultural industry.

Public transportation is the key economic driver of a country. The true measure of a country's progress level is scaled on the number of people using the public transportation rather than of people riding private cars. In the Philippines, Western Visayas region (Region VI) is one of the regions which needs extensive support in public transport data organization. Due to the complexity of a public transport network, handling of big data becomes a bottleneck for transport planners. Addressing this problem will help them move forward to more important tasks such as improving transport service for passengers. In this study, a framework was designed in modeling public transportation data. TigerGraph database was utilized to preconnect data and to allow acquisition of geospatial intelligence on route while Django-python was used as the web framework for the geographic insight web application. With the framework and software solution developed, the study intended to make data organization scalable, visualize data relationships, and preconnect data. Preconnecting data in public transport such as terminals, PUV stops, and facilities in conjunction with massive parallel processing (MPP) function, speeds up data analysis. This also enables expanded capability of a system to return answers to queries which need deeper analysis.

Chlorophyll content is an imperative indicator of lettuce (Lactuca Sativa) health status. Through computational intelligence, this paper bestowed a noninvasive, accurate, cost-effective ensemble of machine learning algorithms for chlorophyll-b concentration prediction. A total of 107 images of loose-leaf lettuce var. Altima from an aquaponic farm situated in Rizal province in the Philippines was utilized. By employing CIELab color space, the leaf canopies were segmented and extracted with 18-feature predictors. The regression tree ranked and selected 10 selected significant leaf features (spectral: R, G, S, a*, b*, Cr; morphological: canopy area; textural: contrast, correlation, and homogeneity). A fitness function that optimized the recurrent neural network architecture was constructed using GPTIPSv2 which is a symbolic multigene regression (SMGR) tool. This convergence function was the main element in developing a genetic algorithm (GA)-optimized recurrent neural network model considered as the PIGMENTnet. It provides the optimal quantity of neurons in each of the three hidden layers in neural network architecture. A 75-100-10 conglomeration of neurons in each layer was recommended. The RMSE (0.1486), R2 (0.9998), and MAE (0.0751) results of PIGMENTnet surpassed the unoptimized RNN. Based on these findings, it implies that the developed PIGMENTnet is an effective Chl-b concentration predictor as it provided highly accurate and sensitive results than the sole RNN model.

Invasive genetic and chemical-based laboratory techniques are very limited for in situ and in vivo applications especially in classifying leaf species in the wild. Out of 41 recorded Salvia species, 25 are endemic to the Central Asian region. In this study, a non-destructive model for profiling and identifying Salvia species (SalviaNet) was developed by employing computer vision allied with feature-based machine learning. The image set is composed of 25 Salvia species collected over Uzbekistan and other territories (Locus classicus) and photo-scanned to capture the totality of the leaf surface. CIELab thresholding was employed to fully segment the leaf pixels. Classification tree (CTree) was used to select the most significant spectro-textural-morphological leaf signatures resulting in only 11 attributes. These leaf signatures were profiled using the distance method with a distance power of 2. Hybrid CTree and linear discriminant analysis (CTree-LDA or SalviaNet) outperformed other computational models in classifying Salvia species based on the accuracy (90.7%) and sensitivity (90.7%). Based on profiling, leaf's red reflectance, compactness, and shape factor 2 are the strong determinants in discriminating Salvia species. Overall, the developed SalviaNet is proven reliable for on-site application and will essentially help the field of plant taxonomy.

Excess nitrate concentration leads to excessive algal growth that reduces dissolved oxygen for aquatic animals. A significant strategy to preserve the water quality of aquatic systems is through nitrate level assessment. However, use of nitrate sensors and existing laboratory approach is costly and requires a huge effort. This study investigated the application of computational intelligence for measurement of nitrate concentration in a tilapia fishpond at Rizal province, Philippines, based on physico-limnological parameters such as temperature, electrical conductivity, and pH level. Artificial neural network (ANN) algorithms including feed-forward (FNN) and recurrent (RNN) neural networks were developed and optimized using genetic algorithm (GA) to improve their predicting performances. Genetic programming (GP), through GPTIPSv2 tool, was configured to generate a fitness function. This function is the principal component of GA optimization to produce optimal number of hidden neurons for ANN architecture that resulted in 2 neurons for GA-FNN and combination of 92, 31, and 11 neurons for each hidden layer using the GA-RNN model. Based on evaluation results, all models provided acceptable results with error and predictive accuracy values approaching 0 and 1, respectively. However, the GA-FNN model outperformed other models with 3.26 RMSE, 2.23 MAE, and 0.97 R2values which proved to be the most effective and suitable model for the indirect measurement of nitrate concentration.

Fish feeding management is one of the most crucial considerations in aquaculture production. The traditional feeding method such as table-based and scheduled automated feeding schemes are inaccurate. In contrast, the automated on-demand feeding system has reduced the inaccuracies of the older feeding schemes. However, existing on-demand systems have limited accessibility because their monitoring systems are only stored by their local devices. This paper proposes an on-demand fish feeding system with online and real-time monitoring using the Internet of Things (IoT) and an accelerometer to sense the fish' demand by hitting it. An overhead surveillance camera was installed on the fish tank to automatically record and monitor the fish feeding activity on the first day of the implementation. Two groups of fish were used for the observation—the adults and pre-growth Nile tilapia (Oreochromis niloticus). Results have shown that the on-demand feeding system is highly effective on 21 pre-growth fish with an average weight of 88 grams and a standard deviation (SD) of ± 39 grams. Additionally, the feed intake ratio (FIR) of the pre-growth fish was$1.35\pm 0.69$grams, i.e., 73% to 86% lower than the recommended table-based feeding scheme. Thus, more efficient.

Food shortage is a serious problem facing the world and is prevalent in urban areas. The scarcity of food is mainly caused by crop failure. Environmental factors offered by the rural areas determine the condition of crops to be produced. This scenario pomps, the explication of urban farming. However, urban farming requires all-out monitoring and control. This study specifically solves the predicament of identifying the developmental growth of plants from seed leaf to amend the techniques of plant science and cultivation management. With a view to this, the paper shows coupled color-based superpixels and multifold watershed transformation in segmenting the lettuce image from the background. To fathom it out, a comparative analysis of three unsupervised machine learning algorithms: Self Organizing Map (SOM), Hierarchical, and K - means algorithms were conducted. These were done by modeling each algorithm from the features extracted from morphological computations of the lettuce images raised in a smart aquaponics setup. Each of the models was optimized to increase cross and hold-out validations. The results showed that K – means algorithm having the parameters of algorithm = ‘auto’, copyx= ‘True’, init = ‘K- means++’, maxiter = ‘1000’, nclusters = ‘3’, ninit = ‘15’, n_jobs = ‘1’, precompute_distance = ‘auto’, random_state = ‘10’, tol = ‘0.000001’, verbose = ‘1’, leaf_size = ‘10’ was the most effective model for the given dataset, yielding a high precision and recall unsupervised clustering percentage of 91%.

Six Sigma is an engineering systematic methodology modeled within the DMAIC course (Define, Measure, Analyze, Improve and Control). It is used for improving high risk areas that can affect the profitability of the organization. This paper aims to prove the flexibility of Six Sigma by increasing the efficiency rate of internal audit team in an existing real-estate company by targeting standard man-days or further reducing it to less than the set timeline. The efficiency rate of the audit team is 41.67% with average man-days of 26.9. Timeliness of audit report has been an integral contributor of on- time resolution of non-conformities and appropriate mitigation of high risks incurred on those findings. Through cause-effect analysis guided by Affinity method probable causes were listed down and for the perceived causes with no measurable data further investigation and walkthrough was done using GEMBA walk. Data were collected and analyzed to validate these causes. Hypothesis tests and control charts like ANOVA test, Ishikawa Diagram and Boxplot were used through the use of Engineering simulation programs, to analyze the gathered data and results revealed the root causes of the problem such as manual extraction of audit evidence, difficulty searching and retrieving records, variation on auditors’ performance, and unawareness of risk management procedures among auditees. Upon implementation of improvement solutions, the average man-days reduced from 26.9 days to 23.8 days translating to an efficiency rate of 80%.

Dehazing through Dark Channel Prior (DCP), originally developed for land-based images, has translated its potential for improving the quality of underwater images. However, the DCP default parameters, which are just adapted from land-based applications, may not be applicable for underwater images. Such constraint limits the capability of this restoration algorithm to improve the quality of an underwater image; the values of these parameters must be searched for each underwater image. A proposed approach on the parameter values assignment problem is to conduct an optimized search based on Genetic Algorithm. The presentation of this proposed approach focuses on the Genetic Algorithm processes: chromosome encoding, fitness function development, and selection, mutation, and crossover, to perform an effective search of the best solution out of a pool of possible solutions. Qualitative and quantitative evaluations show that utilization of optimized combination of DCP parameters, achieves images of higher quality in comparison to the utilization of established default DCP parameters.

Visual inspection of plant health status and disease severity may yield subjective assessments due to error-prone sphere of colors and textures as affected by angular photosynthetic light source and the complexity of chlorosis. Quantification of damages on leaves due to destructive diseases is paramount for plant and pathogen interactions. To address this challenge, the proposed solution is the integration of computer vision and computational intelligence for tomato Septoria leaf spot necrotic and chlorotic region computational assessment. Dataset contains healthy and diseased tomato leaves that were captured individually. Non-vegetation pixels removal was done using CIELab color space. RGB color components and five Haralick texture features were extracted from the segmented leaf. Hybrid neighborhood component analysis and ReliefF algorithm were employed to select the important predictors resulting to RGB-entropy vector. A new tomato leaf disease index (tomLDI) optimized using artificial bee colony (ABC) was developed by normalizing visible red reflectance, and introducing red-green and red-blue reflectance ratios to enhance Septoria leaf spots pixels and reducing sensitivity to healthy green pixels. KNN bested classification tree, linear discriminant analysis and Naïve Bayes in detecting Septoria leaf disease with accuracy of 97.46%. Deep transfer image regression was tested using raw infected leaf images and the tomLDI transformed colored channels through MobileNetV2, ResNet101 and InceptionV3. Using tomLDI channel, MobileNetV2 and ResNet101 bested other networks in estimating leaf diseased region percentage and number of Septoria spots with R2values of 0.9930 and 0.9484 respectively. tomLDI channel proved to be more accurate than using raw images for regression.

Lightness signal and color reflectance constitute the reflected luminance spectra from camera captured image to camera lenses. The intensity of lightness and visible RGB signals deviates as the camera distance to object varies. The presence of uneven distribution of photosynthetic light causes angular light effect of shadowing on the focal object and light emitting objects placed on the visually noisy background added a challenge in materializing an efficient greenness index for crop phenotyping. The proposed method in this study compensates excessive relative brightness on the image by introducing lightness rectification coefficient and employing genetic algorithm to derive a novel visible tetrahedron greenness index (gvTeGI) based on normalized green waveband. Hybrid neighborhood component analysis and Pearson's correlation coefficient approach for feature selection resulted to retaining photosynthetic canopy area, and correlation and homogeneity texture features as highly important descriptors for biophysical signatures considered in this study which are lettuce fresh weight, height and number of spanning leaves. The selection, crossover and mutation rates used to optimize the genetic algorithm model are 0.2, 0.8 and 0.01 respectively. Indoor and outdoor aquaponic system was deployed for 6-week full crop life cycle cultivation. Regression machine learning models were used to estimate biophysical signatures from extracted gvTeGI channels. Optimized Gaussian processing regression model bested regression support vector machine and regression tree in estimating fresh weight, height and number of spanning leaves with R2values of 0.7939, 0.7662 and 0.7446. The proposed gvTeGI proved to be more accurate than previously published greenness index for the estimation of biophysical signatures of lettuce using consumer-grade RGB camera.

Energy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns. In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM. Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.

Identifying variant diseases in leaves is a significant method for optimizing food production. As the global population continues to arise and agricultural space continues to decline, every possible way of increasing the supply of food in any given condition and limited resources will address the above-mentioned problems. This study proposes a way for detecting three different diseases from grape leaves apart from the healthy leaves and considers the confidence value of the system in correctly identifying the classes. The diseases are namely: Black Rot, Black Measles, and Isariopsis. The system conducted a comparative analysis to determine which among the three pre-trained networks, AlexNet, GoogLeNet, and ResNet-18 will be the most suitable network to be integrated with Regions with Convolutional Neural Networks (RCNN) in performing multiple object detection in a given image. The data used in training the models comprised of annotated image data represented as a ground truth table with image files and their corresponding bounding boxes coordinates. The models evaluated resulted to AlexNet being the best pre-trained network to be working on the RCNN with an accuracy of 95.65%. The other two models from GoogLeNet and ResNet-18 only obtained accuracies of 92.29% and 89.49% respectively.

Lettuce is one of the most popular crops for urban farming because it is easy to grow and it has high nutritional value. Moreover, it is adaptable and can be combined with other food options, or it can be eaten alone without too much preparation. Predicting lettuce growth can be crucial to find the optimum maturity and harvest time. This paper proposed to use a model of a hybrid tree-fuzzy logic approach, the classification tree was used to select the most significant features from the texture features then the fuzzy inference system was utilized in predicting the lettuce growth stage classification. The hybrid system produced accurate results with low percentage error and correct classifications. Based on these results, the most accurate prediction can be observed in the head development growth stage; the harvest growth stage has a slight variance, while the vegetative stage has the most variance. Overall, the trained hybrid system is reliable in predicting and identifying lettuce growth stage classification.