Real-time inspection of fruit by computer vision on a mobile harvesting platform under field conditions

2010 ◽  
Vol 6 (1) ◽  
pp. 1-16 ◽  
Author(s):  
S. Cubero ◽  
E. Moltó ◽  
A. Gutiérrez ◽  
N. Aleixos ◽  
O. García-Navarrete ◽  
...  
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Fruits And Vegetables ◽  
Vision System ◽  
Production Costs ◽  
Field Conditions ◽  
Fresh Market ◽  
Citrus Production ◽  
Surface Damages ◽  
Marketable Fruit

The best alternative for reducing citrus production costs is mechanization. Machine vision is a reliable technology for the automatic inspection of fresh fruits and vegetables that can be adapted to harvesting machines. In these, fruits can be inspected before sending them to the packinghouse and machine vision provides important information for subsequent processing and avoids spending further resources in non-marketable fruit. The present work describes a computer vision system installed on a harvesting machine developed jointly by IVIA and a Spanish enterprise. In this machine, hand pickers directly drop the fruit as they collect it, which results in an important increase of productivity. The machine vision system is placed over rollers in order to inspect the produce, and separate those that can be directly sent to the fresh market from those that do not meet minimal quality requirements but can be used by the processing industry, based on color, size and the presence of surface damages. The system was tested under field conditions.

Developments in computer vision system, focusing on its applications in quality inspection of fruits and vegetables-A review

2017 ◽  
Vol 38 (02) ◽  
Author(s):  
Santosh Chopde ◽  
Madhav Patil ◽  
Adil Shaikh ◽  
Bahvesh Chavhan ◽  
Mahesh Deshmukh
Keyword(s):  
Computer Vision ◽  
Fruits And Vegetables ◽  
Vision System ◽  
Cost Effective ◽  
Quality Analysis ◽  
Quality Inspection ◽  
Safety Standards ◽  
Cost Effective Technique ◽  
Recent Developments ◽  
Computer Vision Technology

Quality inspection of food is a tedious and labor intensive process. Ever-increasing population, losses in handling and processing and the increased expectation of food products of high quality and safety standards has raised the need for accurate, fast and objective quality determination methods. Manual quality inspection is a slow, costly, unreliable process and suffers from poor repeatability. Computer vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. Computer vision is a rapid, economic, consistent, objective inspection and evaluation technique. Computer vision has been successfully adopted for the quality analysis of meat and fish, fruits, vegetables and bread with applications ranging from routine inspection to the complex vision guided robotic control. The paper presents the recent developments in computer vision technology along with important aspects of image processing techniques coupled with application of computer vision technology in quality inspection of fruits and vegetables.

scholarly journals Computer Analysis of Architecture Using Automatic Image Understanding

2019 ◽  
Vol 2018 ◽  
Author(s):  
Fan Wei ◽  
Yuan Li ◽  
Lior Shamir
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Machine Vision ◽  
Computer Analysis ◽  
Vision System ◽  
Cognitive Task ◽  
Image Understanding ◽  
New Paradigm ◽  
Complex Cognitive Task ◽  
Architectural Styles

In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available.

Evaluation and Economics of a Machine-Vision Guided Cultivation Program in Broccoli and Lettuce

2010 ◽  
Vol 24 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Steven A. Fennimore ◽  
Laura Tourte ◽  
John S. Rachuy ◽  
Richard F. Smith ◽  
Christina George
Keyword(s):  
Machine Vision ◽  
Weed Management ◽  
Vision System ◽  
Production Costs ◽  
Yield Reduction ◽  
Vegetable Crops ◽  
Net Returns ◽  
Rainy Weather ◽  
Guidance Systems ◽  
Hand Weeding

Machine-vision cultivator guidance systems are commercially available to growers, but little work has been done to determine if these guidance systems can improve integrated weed management systems in vegetable crops. Studies were conducted in 2005 and 2006 in broccoli and lettuce to evaluate band-applied DCPA or pronamide, respectively, and four noncultivated bands ranging from 5.1 to 12.7 cm. DCPA or pronamide were applied in bands centered on the seed line at 0, 7.6 or 12.7 cm wide. A commercial machine-vision system was used to guide a commercial cultivator. Generally, weed densities and hand-weeding times were less where the DCPA band in broccoli or the pronamide band in lettuce were 7.6 or 12.7 cm wide compared to no herbicide. Weed densities were lowest in both crops where the noncultivated band width was 5.1 cm compared to 12.7-cm noncultivated bands. For broccoli in both 2005 and 2006, net returns above production costs were generally higher in the 7.6- and 12.7-cm-wide DCPA bands compared with the no-herbicide band. In lettuce in both years, the no-pronamide treatment had higher net returns, when compared with the 7.6- and 12.7-cm pronamide bands. Lettuce yields and higher net returns in the no-pronamide treatment compared to the 7.6- and 12.7-cm pronamide bands may be due to slight yield reduction from pronamide. Results suggest that pronamide was not needed during the dry months of the year when weed management tools such as hand-weeding and cultivation work very well. However, in periods of rainy weather when cultivation and hand-weeding are not possible, then pronamide would likely provide the greatest economic benefit. Given the large impact of cultivation on vegetable weed management programs, the greatest potential benefit of machine-vision guided cultivators is if they facilitate more timely and effective cultivation.

Ensuring the Safety of UAV Flights by Means of Intellectualization of Control Systems

2019 ◽  
pp. 287-310 ◽  
Author(s):  
Konstantin Dergachov ◽  
Anatolii Kulik
Keyword(s):  
Computer Vision ◽  
Control System ◽  
Machine Vision ◽  
Unmanned Aerial Vehicles ◽  
Vision System ◽  
Visual Estimation ◽  
Computer Vision System ◽  
Estimation Algorithms ◽  
Aerial Vehicles ◽  
Rational Control

In this chapter, the authors present analysis of reasons for deficient safety of unmanned aerial vehicles (UAV) and further ground an approach to improve the safety by intellectualizing operation of the control system. Intellectualization results from the rational control owing to machine vision means used. A conception of building algorithms for visual evaluating position of the UAV that is equipped with a computer vision system is suggested. Algorithms are illustrated by related investigation of an adapted UAV. Both hardware and software means for realizing the visual estimation algorithms are presented.

scholarly journals Bilgisayarlı Görüntüleme Sistemi: Gıda Endüstrisinde Kullanımı ve Uygulamaları

2019 ◽  
Vol 7 (7) ◽  
pp. 989
Author(s):  
Pinar Balkir ◽  
Kemal Kemahlıoğlu ◽  
Ufuk Yücel
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Fruits And Vegetables ◽  
Vision System ◽  
Computer Hardware ◽  
Machine Vision System ◽  
Food Sector ◽  
Image Capture ◽  
Wide Range ◽  
Marine Products

Machine vision system is a combination of camera, image capture card, computer hardware and image processing technology. Safe foods are highly preferred by consumers today and accordingly, machine vision system has the edge on food sector for ensuring qualitative data and accelerating some certain processes. Machine vision system, which is more accurate, reliable and faster than conventional methods, has been used in wide range of applications in the inspection of cereals, fruits and vegetables, meats and marine products and some other processed foods in combination with convenient image processing and analysing algorithms. Considering the objectivity, promptness, economy and effectiveness as the chief advantages, the system makes progress as an alternative method in the sector.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

Establishment and Production Costs for Peach Orchards in Florida: Enterprise Budget and Profitability Analysis

EDIS ◽  
2017 ◽  
Vol 2017 (4) ◽  
Author(s):  
Ariel Singerman ◽  
Marina Burani Arouca ◽  
Mercy A. Olmstead
Keyword(s):  
Discount Rate ◽  
Production Costs ◽  
Fixed Costs ◽  
Average Price ◽  
Land Preparation ◽  
Profitability Analysis ◽  
Enterprise Budget ◽  
Marketable Fruit ◽  
Peach Orchard

The article summarizes the establishment and production costs, as well as the potential profitability of a peach orchard in Florida. Our findings show the initial investment required for a peach operation in Florida to be $6,457 per acre; the expense in land preparation and planting alone in year 1 is $2,541 per acre. Variable and fixed costs in years 2 through 15 average $5,680 per acre. As an example of profitability, when using a 10% discount rate, an operation yielding 6,525 (7,254) pounds of marketable fruit per acre during its most productive years obtains a positive NPV when the average price is $2.38 ($2.13) per pound.

Multifunctional stereoscopic machine vision system with multiple 3D outputs

2020 ◽  
Keyword(s):  
Machine Vision ◽  
International Symposium ◽  
Fast Track ◽  
Vision System ◽  
Machine Vision System ◽  
Stereoscopic Displays ◽  
Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.

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