Fabián Rolando Jiménez López

Bio: Fabián Rolando Jiménez López is an academic researcher from Pedagogical and Technological University of Colombia. The author has contributed to research in topics: Precision agriculture & Global Positioning System. The author has an hindex of 3, co-authored 17 publications receiving 40 citations. Previous affiliations of Fabián Rolando Jiménez López include Universidad Santo Tomás & United States Tennis Association.

Tomato classification according to organoleptic maturity (coloration) using machine learning algorithms K-NN, MLP, and K-Means Clustering

Abstract: This article presents the design, development, implementation and evaluation of different machine learning type algorithms, for Milano and Chonto tomatoes classification, based on the fruit physical characteristics, such as coloring (maturity degree), taking as reference national and international standards (NTC-1103-1 and USDA, respectively). Different digital image processing techniques are shown, used to describe and extract the characteristics of color statistics of the tomatoes images. For data analysis, supervised and /or trained classification algorithms were implemented with databases and features in the RGB, HSI and L*a*b* color spaces. The techniques for classification used and valued were: K-NN (K-Nearest Neighbors), MLP type Neuronal Networks (Multilayer Perceptron) and unsupervised learning algorithms like K-Means. The evaluation of each classification algorithms is shown, using the global confusion matrix, together with performance indices such as accuracy, precision, sensitivity, and specificity.

Biometric iris recognition using Hough Transform

Abstract: This paper describes the segmentation and normalization process for automatic biometric iris recognition system, implemented and validated in MATLAB®. For this work we use the images database digitized in grayscale CASIA v. 2.0, where coding and processing through segmentation algorithms was implemented using Gabor filters and Hough Transform, finally an alternative segmentation algorithm was designed and implemented by the authors, which its performance was evaluated with satisfactory results.

Algorithms to estimation of size and shape tomato using Artificial Vision Techniques

Abstract: This paper describes the development of estimation algorithms for the size and shape of tomato fruits, implemented on a portable electronic system used in greenhouses, with the purpose to classify and collect of tomatoes milano and chonto type. The algorithms were implemented in a Raspberry-Pi embedded card through the use of free software libraries (Python, OpenCV, Pillow and Scikit-Image) and using a Pi-Camera for real-time processing, taking into account the parameters of classification defined in national (NTC1103-1) and international (USDA) standards. Were applied classification algorithms based on Canny to estimate the size of the tomato fruit, as well as techniques based on estimation of the eccentricity and statistical moments descriptors for the measurement of the shape, which were evaluated with a performance superior to 90 %. Classification techniques of tomato shape and size using artificial vision overcame subjective techniques of visual and tactile type classification carried out by farmers and specialized technicians. Keywords-Fruit Classification System, Canny Algorithm, Statistical Moments Techniques, Eccentricity Algorithm, Artificial Vision, Process Automation.

Simulation of the Model, Design and Control of a Current Source Inverter with Unipolar SPWM Modulation

Abstract: This article presents the topology, large and small signal analysis of the mathematical model, sizing of passive components and design of a proportional resonant controller for a Current Source Inverter. The performance of the inverter controller was verified by simulation, obtaining satisfactory results in terms of the harmonic distortion factor in the voltage output and the regulation indices for the load with values close to the operating point. The response of the control system in static considerations and operation dynamic demonstrated its viability for electronic implementation in the future.

Temperature telemetry system for agricultural crops using free software

Abstract: Through this paper, specifies the features and functionality of a software application developed using Python for the acquisition and storage of temperature data with applications in precision agriculture. The telemetry system use Global Positioning Systems (GPS) and Geographic Information Systems (GIS) platforms.

Biometric iris recognition using radial basis function neural network

Abstract: The consistent and efficient method for the identification of biometrics is the iris recognition in view of the fact that it has richness in texture information. A good number of features performed in the past are built on handcrafted features. The proposed method is based on the feed-forward architecture and uses k-means clustering algorithm for the iris patterns classification. In this paper, segmentation of iris is performed using the circular Hough transform that realizes the iris boundaries in the eye and isolates the region of iris with no eyelashes and other constrictions. Moreover, Daugman’s rubber sheet model is used to transform the resultant iris portion into polar coordinates in the process of normalization. A unique iris code is generated by log-Gabor filter to extract the features. The classification is achieved using neural network structures, the feed-forward neural network and the radial basis function neural network. The experiments have been conducted using the Chinese Academy of Sciences Institute of Automation (CASIA) iris database. The proposed system decreases computation time, size of the database and increases the recognition accuracy as compared to the existing algorithms.

Deepblueberry: Quantification of Blueberries in the Wild Using Instance Segmentation

Abstract: An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device. In order to quantify the number of berries per image, a network based on Mask R-CNN for object detection and instance segmentation was proposed. Several backbones such as ResNet101, ResNet50 and MobileNetV1 were tested. The performance of the algorithm was evaluated using the Intersection over Union Error (IoU) and the competitive mean Average Precision (mAP) per images and per batch. The best detection result was obtained with the ResNet50 backbone achieving a mIoU score of 0.595 and mAP scores of 0.759 and 0.724 respectively (for IoU thresholds 0.5 and 0.7). For instance segmentation, the best results obtained were 0.726 for the mIoU metric and 0.909 and 0.774 for the mAP metric using thresholds of 0.5 and 0.7 respectively.

A review on Iris Recognition

Abstract: Iris Recognition has gained a great attention in various fields like industrial areas, security prone areas, border areas and medical institutes etc. Due to its high accuracy and uniqueness, it is used in various fields of access control and security at border areas. The demand for iris recognition is increasing day by day due to its reliability, accuracy and uniqueness. It is the most powerful identification feature among all other biometric features as human iris remains unchanged during whole of the life. For efficient functioning of iris recognition system, researchers have to work on various challenges like images taken in unconstrained environment, noisy images, blurred images and many more. The goal of this review paper is to discuss steps involved in iris recognition system and various techniques used by different researchers for each recognition step.

Reduction of Postharvest Quality Loss and Microbiological Decay of Tomato "Chonto" (Solanum lycopersicum L.) Using Chitosan-E Essential Oil-Based Edible Coatings under Low-Temperature Storage.

Abstract: The tomato (Solanum lycopersicum L.) is one of the many essential vegetables around the world due to its nutritive content and attractive flavor. However, its short shelf-life and postharvest losses affect its marketing. In this study, the effects of chitosan-Ruta graveolens (CS + RGEO) essential oil coatings on the postharvest quality of Tomato var. “chonto” stored at low temperature (4 °C) for 12 days are reported. The film-forming dispersions (FFD) were eco-friendly synthesized and presented low viscosities (between 0.126 and 0.029 Pa s), small particle sizes (between 1.29 and 1.56 μm), and low densities. The mature index (12.65% for uncoated fruits and 10.21% for F4 coated tomatoes), weight loss (29.8% for F1 and 16.7% for F5 coated tomatoes), and decay index (3.0 for uncoated and 1.0 for F5 coated tomatoes) were significantly different, indicating a preservative effect on the quality of the tomato. Moreover, aerobic mesophilic bacteria were significantly reduced (in five Log CFU/g compared to control) by using 15 μL/mL of RGEO. The coatings, including 10 and 15 μL/mL of RGEO, completely inhibited the mold and yeast growth on tomato surfaces without negatively affecting the consumer acceptation, as the sensorial analysis demonstrated. The results presented in this study show that CS + RGEO coatings are promising in the postharvest treatment of tomato var. “chonto”.

IHDS: Intelligent Harvesting Decision System for Date Fruit Based on Maturity Stage Using Deep Learning and Computer Vision

Abstract: Date is the main fruit crop of the Kingdom of Saudi Arabia (KSA), approximately covering 72% of the total area under permanent crops. The Food and Agriculture Organization states that date production worldwide was 3,430,883 tons in 1990, which increases yearly, reaching 8,526,218 tons in 2018. Date production in KSA was around 527,881 tons in 1990, approximately reaching 1,302,859 tons in 2018. Harvesting date fruits at an appropriate time according to a specific maturity stage or level is a critical decision that significantly affects profit. In the present study, we proposed an intelligent harvesting decision system (IHDS) based on date fruit maturity level. The proposed decision system used computer vision and deep learning (DL) techniques to detect seven different maturity stages/levels of date fruit (Immature stage 1, Immature stage 2, Pre-Khalal, Khalal, Khalal with Rutab, Pre-Tamar, and Tamar). In the IHDS, we developed six different DL systems, and each one produced different accuracy levels in terms of the seven aforementioned maturity stages. The IHDS used datasets that have been collected by the Center of Smart Robotics Research. The maximum performance metrics of the proposed IHDS were 99.4%, 99.4%, 99.7%, and 99.7% for accuracy, F1 score, sensitivity (recall), and precision, respectively.