A study on the application of Gravitational Search Algorithm in optimizing Stereo Matching Algorithm's parameters for star fruit inspection system

This paper reports the result obtained by implementing Gravitational Search Algorithm for tuning Stereo Matching Algorithm's parameters for the application star fruit inspection system. The hardware for the inspection system is built by CvviP from Universiti Teknologi Malaysia using only single camera. The implemented Stereo Matching Algorithm used on the system comes from the built-in Matlab library. Each agent of Gravitational Search Algorithm in the search pace represents a set of candidate numerical value of the stereo matching's parameters. The sum of absolute error of the gray scale value of both images is used to indicate the fitness function. Benchmarking has done by comparing the result obtained with the previous literature that implements Particle Swarm Optimization. The result indicates that the application of Gravitational Search Algorithm as parameters tuner for stereo matching's parameters tuning is essentially on par with the Particle Swarm Optimization Algorithm

Desarrollo de algoritmo y prototipo móvil para medir el grado de madurez del aguacate Hass mediante procesamiento digital de imágenes

La producción de aguacate Hass está presente en diferentes regiones de Colombia. Los agricultores cosechan el aguacate cuando ha alcanzado su madurez fisiológica y desde allí puede ser dispendioso conocer su estado de madurez para los comercializadores o consumidores. Por ello se hace necesario tener conocimiento sobre el estado de maduración del fruto con ayuda de herramientas tecnológicas para facilitar su clasificación en base a su madurez y determinar su tiempo de vida, proporcionando detalles precisos para su exportación y venta regional. Los principales criterios de maduración son el cambio de color y pérdida de brillo de la fruta, los cuales pueden ser poco precisos debido a la subjetividad de cada persona. La idea principal es capturar el color y brillo del aguacate por medio de imágenes digitales para analizar su estado y obtener su clasificación

A Study On The Application Of Gravitational Search Algorithm In Optimizing Stereo Matching Algorithm’s Parameters For Star Fruit Inspection System

This paper reports the result obtained by implementing Gravitational Search Algorithm for tuning Stereo Matching Algorithm’s parameters for the application star fruit inspection system. The hardware for the inspection system is built by CvviP from Universiti Teknologi Malaysia using only single camera. The implemented Stereo Matching Algorithm used on the system comes from the built-in Matlab library. Each agent of Gravitational Search Algorithm in the search pace represents a set of candidate numerical value of the stereo matching’s parameters. The sum of absolute error of the gray scale value of both images is used to indicate the fitness function. Benchmarking has done by comparing the result obtained with the previous literature that implements Particle Swarm Optimization. The result indicates that the application of Gravitational Search Algorithm as parameters tuner for stereo matching’s parameters tuning is essentially on par with the Particle Swarm Optimization Algorithm

Advances in non-destructive early assessment of fruit ripeness towards defining optimal time of harvest and yield prediction—a review

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre-and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in-field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshousassessment. This review focuses on the non-destructive methods which are promising for, or have already been applied to, the pre-harvest in-field measurements including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed

Ripeness Classification Of Oil Palm Fruit To Ensure Optimum Quantity Of Oil Using Image Processing Techniques

The project involves on detecting optimum quantity and quality of oil based on the ripeness of oil palm fruit using suitable Digital Image Processing Techniques. The objective of this project is to develop an easy and flexible system where user can use the system to classify the maturity of fruit using CCD camera and Matlab-Image Processing Toolbox

Applications of Image Processing for Grading Agriculture products

Image processing in the context of Computer vision, is one of the renowned topic of computer science and engineering, which has played a vital role in automation. It has eased in revealing unknown fact in medical science, remote sensing, and many other domains. Digital image processing along with classification and neural network algorithms has enabled grading of various things. One of prominent area of its application is classification of agriculture products and especially grading of seed or cereals and its cultivars. Grading and sorting system allows maintaining the consistency, uniformity and depletion of time. This paper highlights various methods used for grading various agriculture products. DOI: 10.17762/ijritcc2321-8169.15036

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

This study presents a method for enhancing the accuracy of object detection in industrial automation applications using ResNet18-based image segmentation. The objective is to extract object images from the background image accurately and efficiently. The study includes three experiments, RGB to grayscale conversion, single image processing, and dual image processing. The results of the experiments show that dual image processing is superior to both RGB to grayscale conversion and single image processing techniques in accurately identifying object edges, determining CG values, and cutting background images and gripper heads. The program achieved a 100% success rate for objects located in the workpiece tray, while also identifying the color and shape of the object using ResNet-18. However, single image processing may have advantages in certain scenarios with sufficient image information and favorable lighting conditions. Both methods have limitations, and future research could focus on further improvements and optimization of these methods, including separating objects into boxes of each type and converting image coordinate data into robot working area coordinates. Overall, this study provides valuable insights into the strengths and limitations of different object recognition techniques for industrial automation applications

A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable

An embedded real-time red peach detection system based on an OV7670 camera, ARM Cortex-M4 processor and 3D Look-Up Tables

This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second