Analysis & Numerical Simulation of Indian Food Image Classification Using Convolutional Neural Network

Recognition of Indian food can be assumed to be a fine-grained visual task owing to recognition property of various food classes. It is therefore important to provide an optimized approach to segmentation and classification for different applications based on food recognition. Food computation mainly utilizes a computer science approach which needs food data from various data outlets like real-time images, social flat-forms, food journaling, food datasets etc, for different modalities. In order to consider Indian food images for a number of applications we need a proper analysis of food images with state-of-art-techniques. The appropriate segmentation and classification methods are required to forecast the relevant and upgraded analysis. As accurate segmentation lead to proper recognition and identification, in essence we have considered segmentation of food items from images. Considering the basic convolution neural network (CNN) model, there are edge and shape constraints that influence the outcome of segmentation on the edge side. Approaches that can solve the problem of edges need to be developed; an edge-adaptive As we have solved the problem of food segmentation with CNN, we also have difficulty in classifying food, which has been an important area for various types of applications. Food analysis is the primary component of health-related applications and is needed in our day to day life. It has the proficiency to directly predict the score function from image pixels, input layer to produce the tensor outputs and convolution layer is used for self- learning kernel through back-propagation. In this method, feature extraction and Max-Pooling is considered with multiple layers, and outputs are obtained using softmax functionality. The proposed implementation tests 92.89% accuracy by considering some data from yummly dataset and by own prepared dataset. Consequently, it is seen that some more improvement is needed in food image classification. We therefore consider the segmented feature of EA-CNN and concatenated it with the feature of our custom Inception-V3 to provide an optimized classification. It enhances the capacity of important features for further classification process. In extension we have considered south Indian food classes, with our own collected food image dataset and got 96.27% accuracy. The obtained accuracy for the considered dataset is very well in comparison with our foregoing method and state-of-the-art techniques.

객체 인식의 레이블 효율적 학습

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2023. 2. 윤성로.딥러닝의 발전은 이미지 물체 인식 분야를 크게 발전시켰다. 하지만 이러한 발전은 수많은 학습 이미지와 각 이미지에 사람이 직접 생성한 물체의 위치 정보에 대한 레이블 덕분에 가능한 것이였다. 이미지 물체 인식 분야를 실생활에서 활용하기 위해서는 다양한 물체의 카테고리를 인식 할 수 있어야 하며, 이를 위해선 각 카테고리당 수많은 학습 데이터가 필요하다. 하지만 각 이미지당 물체의 위치를 각 픽셀마다 주석을 다는 것은 많은 비용이 들어간다. 이러한 정보를 얻을 때 필요한 비용은 약한지도학습으로 줄일 수 있다. 약한 지도 학습이란, 물체의 명시적인 위치 정보를 포함하는 레이블보다 더 값싸게 얻을 수는 있지만, 약한 위치 정보를 활용하여 뉴럴네트워크를 학습하는 것이다. 본 학위논문에서는 물체의 카테고리 정보, 학습 외 분포 데이터 (out-of-distribution) 데이터, 그리고 물체의 박스 레이블을 활용하는 약한지도학습 방법론들을 다룬다. 첫 번째로, 물체의 카테고리 정보를 이용한 약한 지도 학습을 다룬다. 대부분의 카테로기 정보를 활용하는 방법들은 학습된 분류기로부터 얻어진 기여도맵 (attribution map) 을 활용하지만, 이들은 물체의 일부만을 찾아내는 문제가 있다. 우리는 이 문제에 대한 근본 원인을 이론적인 관점에서 의논하고, 이 문제를 해결할 수 있는 세 가지의 방법론을 제안한다. 하지만, 물체의 카테고리 정보만 활용하게 되면 이미지의 전경과 배경이 악의적인 상관관계를 가진다고 잘 알려져 있다. 우리는 이러한 상관관계를 학습 외 분포 데이터를 활용하여 완화한다. 마지막으로, 물체의 카테고리 정보에 기반한 방법론들은 같은 카테고리의 다른 물체를 분리하지 못하기 때문에 인스턴스 분할 (instance segmentation) 에 적용되기는 힘들다. 따라서 물체의 박스 레이블을 활용한 약한 지도학습 방법론을 제안한다. 제안된 방법론을 통해 레이블을 제작하는 시간을 획기적으로 줄일 수 있다는 것을 실험결과를 통해 확인했다. 어려운 데이터셋인 Pascal VOC 에 대해 우리는 91%의 데이터 비용을 감소하면서, 강한 레이블로 학습된 비교군의 89%의 성능을 달성하였다. 또한, 물체의 박스 정보를 활용해서는 83% 의 데이터 비용을 감소하면서, 강한 레이블로 학습된 비교군의 96%의 성능을 달성하였다. 본 학위논문에서 제안된 방법론들이 딥러닝 기반의 물체 인식이 다양한 데이터와 다양한 환경에서 활용되는 데에 있어 도움이 되기를 기대한다.Advances in deep neural network approaches have produced tremendous progress in object recognition tasks, but it has come at the cost of annotating a huge amount of training images with explicit localization cues. To use object recognition tasks in real-life applications requires a large variety of object classes and a great deal of labeled data for each class. However, labeling pixel-level annotations of each object class is laborious, and hampers the expansion of object classes. The need for such expensive annotations is sidestepped by weakly supervised learning, in which a DNN is trained on images with some form of abbreviated annotation that is cheaper than explicit localization cues. In the dissertation, we study the methods of using various form of weak supervision, i.e., image-level class labels, out-of-distribution data, and bounding box labels. We first study image-level class labels for weakly supervised semantic segmentation. Most of the weakly supervised methods on image-level class labels depend on attribution maps from a trained classifier, but their focus tends to be restricted to a small discriminative region of the target object. We theoretically discuss the root cause of this problem, and propose three novel techniques to address this issue. However, built on class labels only, the produced localization maps are known to suffer from the confusion between foreground and background cues, i.e., spurious correlation. We address the spurious correlation problem by utilizing out-of-distribution data. Finally, methods based on class labels cannot separate different instance objects of the same class, which is essential for instance segmentation. Therefore, we utilize bounding box labels for weakly supervised instance segmentation as boxes provide information about individual objects and their locations. Experimental results show that annotation cost for learning semantic segmentation and instance segmentation can be significantly reduced: On the challenging Pascal VOC dataset, we have achieved 89% of the performance of the fully supervised equivalent by using only class labels, which reduces the label cost by 91%. In addition, we have achieved 96% of the performance of the fully supervised equivalent by using bounding box labels, which reduces the label cost by 83%. We expect that the methods introduced in this dissertation will be helpful for applying deep learning based object recognition tasks in a variety of domains and scenarios.1 Introduction 1 2 Background 8 2.1 Object Recognition 8 2.2 Weak Supervision 13 2.3 Preliminary Algirothms 16 2.3.1 Attribution Methods for Image Classifier 16 2.3.2 Refinement Techniques of Localization Maps 18 3 Learning with Image-Level Class Labels 22 3.1 Introduction 22 3.2 Related Work 23 3.2.1 FickleNet: Stochastic Inference Approach 23 3.2.2 Other Recent Approaches 26 3.3 Anti-Adversarially Manipulated Attribution 28 3.3.1 Adversarial Attack 28 3.3.2 Proposed Method 29 3.3.3 Experiments 33 3.3.4 Discussion 36 3.3.5 Analysis of Results by Class 42 3.4 Reducing Information Bottleneck 46 3.4.1 Information Bottleneck 46 3.4.2 Motivation 47 3.4.3 Proposed Method 49 3.4.4 Experiments 52 3.5 Summary 60 4 Learning with Auxiliary Data 62 4.1 Introduction 62 4.2 Related Work 65 4.3 Methods 66 4.3.1 Collecting the Hard Out-of-Distribution Data 67 4.3.2 Learning with the Hard Out-of-Distribution Data 69 4.3.3 Training Segmentation Networks 71 4.4 Experiments 73 4.4.1 Experimental Setup 73 4.4.2 Experimental Results 73 4.4.3 Analysis and Discussion 76 4.5 Analysis of OoD Collection Process 81 4.6 Integrating Proposed Methods 82 4.7 Summary 83 5 Learning with Bounding Box Labels 85 5.1 Introduction 85 5.2 Related Work 87 5.3 Methods 89 5.3.1 Revisiting Object Detectors 89 5.3.2 Bounding Box Attribution Map 90 5.3.3 Training the Segmentation Network 91 5.4 Experiments 93 5.4.1 Experimental Setup 93 5.4.2 Weakly Supervised Instance Segmentation 94 5.4.3 Weakly Supervised Semantic Segmentation 96 5.4.4 Ablation Study 98 5.5 Detailed Analysis of the BBAM 100 5.6 Summary 104 6 Conclusion 105 6.1 Dissertation Summary 105 6.2 Limitations and Future Direction 107 Abstract (In Korean) 133박

Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods

Computer vision based classification of fruits and vegetables for self-checkout at supermarkets

The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, object recognition, Content Based Image Retrieval (CBIR), image segmentation and terrestrial analysis from space (i.e. crop estimation). Despite significant prior research, the area of object recognition still has many topics to be explored. This PhD thesis focuses on using advanced machine learning approaches to enable the automated recognition of fresh produce (i.e. fruits and vegetables) at supermarket self-checkouts. This type of complex classification task is one of the most recently emerging applications of advanced computer vision approaches and is a productive research topic in this field due to the limited means of representing the features and machine learning techniques for classification. Fruits and vegetables offer significant inter and intra class variance in weight, shape, size, colour and texture which makes the classification challenging. The applications of effective fruit and vegetable classification have significant importance in daily life e.g. crop estimation, fruit classification, robotic harvesting, fruit quality assessment, etc. One potential application for this fruit and vegetable classification capability is for supermarket self-checkouts. Increasingly, supermarkets are introducing self-checkouts in stores to make the checkout process easier and faster. However, there are a number of challenges with this as all goods cannot readily be sold with packaging and barcodes, for instance loose fresh items (e.g. fruits and vegetables). Adding barcodes to these types of items individually is impractical and pre-packaging limits the freedom of choice when selecting fruits and vegetables and creates additional waste, hence reducing customer satisfaction. The current situation, which relies on customers correctly identifying produce themselves leaves open the potential for incorrect billing either due to inadvertent error, or due to intentional fraudulent misclassification resulting in financial losses for the store. To address this identified problem, the main goals of this PhD work are: (a) exploring the types of visual and non-visual sensors that could be incorporated into a self-checkout system for classification of fruits and vegetables, (b) determining a suitable feature representation method for fresh produce items available at supermarkets, (c) identifying optimal machine learning techniques for classification within this context and (d) evaluating our work relative to the state-of-the-art object classification results presented in the literature. An in-depth analysis of related computer vision literature and techniques is performed to identify and implement the possible solutions. A progressive process distribution approach is used for this project where the task of computer vision based fruit and vegetables classification is divided into pre-processing and classification techniques. Different classification techniques have been implemented and evaluated as possible solution for this problem. Both visual and non-visual features of fruit and vegetables are exploited to perform the classification. Novel classification techniques have been carefully developed to deal with the complex and highly variant physical features of fruit and vegetables while taking advantages of both visual and non-visual features. The capability of classification techniques is tested in individual and ensemble manner to achieved the higher effectiveness. Significant results have been obtained where it can be concluded that the fruit and vegetables classification is complex task with many challenges involved. It is also observed that a larger dataset can better comprehend the complex variant features of fruit and vegetables. Complex multidimensional features can be extracted from the larger datasets to generalise on higher number of classes. However, development of a larger multiclass dataset is an expensive and time consuming process. The effectiveness of classification techniques can be significantly improved by subtracting the background occlusions and complexities. It is also worth mentioning that ensemble of simple and less complicated classification techniques can achieve effective results even if applied to less number of features for smaller number of classes. The combination of visual and nonvisual features can reduce the struggle of a classification technique to deal with higher number of classes with similar physical features. Classification of fruit and vegetables with similar physical features (i.e. colour and texture) needs careful estimation and hyper-dimensional embedding of visual features. Implementing rigorous classification penalties as loss function can achieve this goal at the cost of time and computational requirements. There is a significant need to develop larger datasets for different fruit and vegetables related computer vision applications. Considering more sophisticated loss function penalties and discriminative hyper-dimensional features embedding techniques can significantly improve the effectiveness of the classification techniques for the fruit and vegetables applications

A quantification model against tuta absoluta effects on tomato plants: a computer vision approach

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyTomatoes are among the most commonly cultivated crops in the world. It is considered a high value crop and income resource for smallholder farmers in Africa. Nevertheless, its production currently endangered by Tuta absoluta pest. The pest has severely damaged tomato yields to the extent that growers are giving up tomato production due to the high costs and losses incurred. It causes a heavy loss in tomato produce ranging from 80 to 100% when not effectively managed. Recently, farmers have been using different methods in efforts to control the pest. These include using pheromone traps and natural enemies for population monitoring, planting resistant tomato varieties, and continuous spraying of chemical pesticides, which is now the main control method. These practices have been proven not to be effective in controlling the pest; they are time-consuming and relatively expensive. Inspired by the progression and positive outcomes of computer vision methods in diagnosing a wide variety of plant diseases and pests, this study proposes a segmentation-based quantification model for detecting and quantifying Tuta absoluta’s damage to tomato plants. We develop convolutional neural network models based on U-Net and Mask RCNN architectures for automatic semantic and instance segmentation respectively using data collected from the field. Experimental results show that Mask RCNN achieved a mAP of 85.67% and U-Net obtained 78.60% and 82.86% of Jaccard index and Dice Coefficient respectively. Both models were precise in segmenting the shapes of Tuta absoluta-infected areas in tomato leaves and determine their extent of the damage. The model was then deployed on the mobile phone to enable farmers and extension officers in Tanzania to automatically detect affected areas on tomato plants and make informed decisions on how to control the pest so as to increase tomato production and save farmers from the losses they face

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

On incorporating inductive biases into deep neural networks

A machine learning (ML) algorithm can be interpreted as a system that learns to capture patterns in data distributions. Before the modern \emph{deep learning era}, emulating the human brain, the use of structured representations and strong inductive bias have been prevalent in building ML models, partly due to the expensive computational resources and the limited availability of data. On the contrary, armed with increasingly cheaper hardware and abundant data, deep learning has made unprecedented progress during the past decade, showcasing incredible performance on a diverse set of ML tasks. In contrast to \emph{classical ML} models, the latter seeks to minimize structured representations and inductive bias when learning, implicitly favoring the flexibility of learning over manual intervention. Despite the impressive performance, attention is being drawn towards enhancing the (relatively) weaker areas of deep models such as learning with limited resources, robustness, minimal overhead to realize simple relationships, and ability to generalize the learned representations beyond the training conditions, which were (arguably) the forte of classical ML. Consequently, a recent hybrid trend is surfacing that aims to blend structured representations and substantial inductive bias into deep models, with the hope of improving them. Based on the above motivation, this thesis investigates methods to improve the performance of deep models using inductive bias and structured representations across multiple problem domains. To this end, we inject a priori knowledge into deep models in the form of enhanced feature extraction techniques, geometrical priors, engineered features, and optimization constraints. Especially, we show that by leveraging the prior knowledge about the task in hand and the structure of data, the performance of deep learning models can be significantly elevated. We begin by exploring equivariant representation learning. In general, the real-world observations are prone to fundamental transformations (e.g., translation, rotation), and deep models typically demand expensive data-augmentations and a high number of filters to tackle such variance. In comparison, carefully designed equivariant filters possess this ability by nature. Henceforth, we propose a novel \emph{volumetric convolution} operation that can convolve arbitrary functions in the unit-ball ($\mathbb{B}^3$) while preserving rotational equivariance by projecting the input data onto the Zernike basis. We conduct extensive experiments and show that our formulations can be used to construct significantly cheaper ML models. Next, we study generative modeling of 3D objects and propose a principled approach to synthesize 3D point-clouds in the spectral-domain by obtaining a structured representation of 3D points as functions on the unit sphere ($\mathbb{S}^2$). Using the prior knowledge about the spectral moments and the output data manifold, we design an architecture that can maximally utilize the information in the inputs and generate high-resolution point-clouds with minimal computational overhead. Finally, we propose a framework to build normalizing flows (NF) based on increasing triangular maps and Bernstein-type polynomials. Compared to the existing NF approaches, our framework consists of favorable characteristics for fusing inductive bias within the model i.e., theoretical upper bounds for the approximation error, robustness, higher interpretability, suitability for compactly supported densities, and the ability to employ higher degree polynomials without training instability. Most importantly, we present a constructive universality proof, which permits us to analytically derive the optimal model coefficients for known transformations without training

Implementation of an IoT ecosystem for controlling calorie intake through deep learning mechanisms

Trabajo de Fin de Máster en Internet de las Cosas, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2020/2021El presente trabajo propone una solución inteligente dentro del paradigma IoT (Internet of Things). Se trata de un diseño que clasifica imágenes de alimentos capturadas con la cámara de un dispositivo móvil según el tipo de alimento a la vez que se obtienen las calorías asignadas al mismo con el fin de llevar un control sobre la ingesta de calorías en el tiempo. La solución inteligente se centra en la utilización de dos modelos de redes neuronales convolucionales, concretamente AlexNet y GoogLeNet, que se adaptan y se rediseñan de acuerdo con las especificaciones de la aplicación, más concretamente para la clasificación de diez categorías de alimentos distintas, a partir de los cuales se puede determinar su nivel de calorías. Son modelos pre-entrenados, que se re-entrenan para las imágenes propias en lo que se conoce como transferencia de aprendizaje. Los resultados de la clasificación se envían a ThingSpeak, que es una plataforma remota para almacenamiento de datos y procesamiento en la nube específicamente diseñada para aplicaciones IoT. De esta forma es posible monitorizar la trazabilidad de los datos almacenados, principalmente la ingesta de calorías. La aplicación, basada en distintos componentes de Matlab, consta de un módulo de captura de imágenes a través de la cámara de un dispositivo móvil, que tiene a la vez instalada una aplicación con capacidad de comunicación on-line, tanto con un computador central como con los servicios en la nube de Matlab (Drive) o la mencionada plataforma ThingSpeak, desde donde se pueden enviar alarmas o avisos vía Twitter. Los distintos módulos, convenientemente integrados, constituyen la aplicación en su conjunto, que permite determinar su validez mediante el análisis de los resultados obtenidos.The present project proposes an intelligent solution under the Internet of Things (IoT) paradigm. It is a design which classifies food images captured with the camera of a mobile device according to the type of food while obtaining the calories assigned to it in order to keep track of calorie intake over time. This intelligent solution focuses on the use of two convolutional neural network models, particularly AlexNet and GoogLeNet, which are adapted, redesigned according to the application specifications, those being the classification of ten different categories of food, from which the level of calories can be determined. These are pre-trained models, which are re-trained with images in what is known as transfer learning. The results of the classification are then sent to ThingSpeak, which is a remote platform for data storage and cloud processing specifically designed for IoT applications. Thus, it is possible to monitor the traceability of the stored data, mainly the calorie intake. The application, based on different Matlab components, consists of an image capturing module by using the camera of a mobile device, which also has installed an application with on-line communication capacity, both with a central computer, Matlba’s cloud services (Drive) and the aforementioned ThingSpeak platform, from which alarms or warnings can be sent via Twitter. The different modules, conveniently integrated, constitute the application as a whole, which makes it possible to determine its validity by analyzing the results obtained.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu