Classification and Evaluation of Tomato Images Using Several Classifier

Asadollahi, Hassan; Kamarposhty, Morteza Sabery; Teymoori, Mir Majid

doi:10.1109/iacsit-sc.2009.47

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…The first notable work carried out in this regard was in the year 1991, where [10] explored a method to perform quality checks on conveyor belts using image processing and a 3D-LUT based technique with the help of a CCD camera. [11], [12] and [13] also discuss various methods of quality checking of produce with respect to a production line in an industry. These methods though proving to be a viable and accurate solution with respect to a manufacturing plant, can in turn be a highly impractical solution for the farmer due to initial costs and also maintenance.…”

Section: Introductionmentioning

confidence: 99%

A cost effective tomato maturity grading system using image processing for farmers

Rupanagudi¹,

Ranjani²,

Nagaraj³

et al. 2014

2014 International Conference on Contemporary Computing and Informatics (IC3I)

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Maturity grading or in other words classifying the ripeness of a fruit, based on its color or texture, forms a very important process to be carried out by agriculturists and the food processing industry worldwide. Current techniques mainly involve manual inspection, which leads to erroneous classification, which in turn would cause economical losses due to inferior produce entering the market chain. A loss of yield during storage may also occur with this type of classification, since it would lead to wrong expiry date predictions as well. Several methodologies to automate this process exist but involve highly expensive setups and complicated procedures which are not a viable solution, especially for the agriculturists of a developing nation. In this paper we discuss a cost effective maturity grading system for one of the most popular fruit in the world -the tomato. A novel setup utilizing inexpensive material and image processing algorithms to identify the six important stages of tomato ripening have been presented. All algorithms were designed and developed using Simulink, a part of MATLAB 2011b on a 2.5 GHz CPU. An overall 98% accuracy was achieved with respect to maturity grade detection and an execution speed of greater than 7.6 times was obtained in comparison with two other popular methodologies.

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Section: Introductionmentioning

confidence: 99%

A cost effective tomato maturity grading system using image processing for farmers

Rupanagudi¹,

Ranjani²,

Nagaraj³

et al. 2014

2014 International Conference on Contemporary Computing and Informatics (IC3I)

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“…Moreover, by reducing the complexity of the algorithms, the process can be performed by the commercial electronic equipment available, and which has fewer requirements than those designed with systems similar to the ones considered by Laykin et al (2002) or Asadollahi et al (2009) among others.…”

Section: Discussionmentioning

confidence: 99%

“…One of the largest concentrations of greenhouses in the Mediterranean are the coastal areas of High speed intelligent classifier of tomatoes algorithms considered most appropriate for the assessment of tomatoes and do so with great accuracy (Laykin et al, 2002), even recommending what type of sorting algorithm is most efficient and achieves fewest errors (Asadollahi et al, 2009). This paper will present the development and evaluation of a low investment classification system of tomatoes with these objectives: to put it at the service of producing farms and to classify for trading standards.…”

Section: Introductionmentioning

confidence: 99%

High speed intelligent classifier of tomatoes by colour, size and weight

Clement¹,

Novas²,

Gázquez³

et al. 2012

Span. j. agric. res.

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At present most horticultural products are classified and marketed according to quality standards, which provide a common language for growers, packers, buyers and consumers. The standardisation of both product and packaging enables greater speed and efficiency in management and marketing. Of all the vegetables grown in greenhouses, tomatoes are predominant in both surface area and tons produced. This paper will present the development and evaluation of a low investment classification system of tomatoes with these objectives: to put it at the service of producing farms and to classify for trading standards. An intelligent classifier of tomatoes has been developed by weight, diameter and colour. This system has optimised the necessary algorithms for data processing in the case of tomatoes, so that productivity is greatly increased, with the use of less expensive and lower performance electronics. The prototype is able to achieve very high speed classification, 12.5 ratings per second, using accessible and low cost commercial equipment for this. It decreases fourfold the manual sorting time and is not sen sitive to the variety of tomato classified. This system facilitates the processes of standardisation and quality control, increases the competitiveness of tomato farms and impacts positively on profitability. The automatic classification system described in this work represents a contribution from the economic point of view, as it is profitable for a farm in the short term (less than six months), while the existing systems, can only be used in large trading centers.Additional key words: artificial vision; low cost; Solanum lycopersicum; trading. Resumen Clasificadora inteligente de tomates de alta velocidad por color, tamaño y pesoEn la actualidad la mayoría de los productos hortofrutícolas son clasificados y comercializados según unos estándares de calidad, que proporcionan un lenguaje común para productores, empacadores, compradores y consumidores. De todas las hortalizas cultivadas en invernadero, el tomate es el que más presencia tiene en superficie y toneladas producidas. En este trabajo se desarrolla y evalúa un sistema de clasificación de tomates de bajo coste con dos objetivos: que sirva a los productores de tomate y que sea capaz de clasificar según los estándares establecidos. En este trabajo se desarrolla un clasificador inteligente de tomates según su peso, diámetro y color. Este sistema optimiza los algoritmos de análisis para el caso específico del tomate, con la ventaja del uso de electrónica de bajo coste. El prototipo es capaz de conseguir la clasificación a velocidad muy elevada, 12.5 clasificaciones por segundo, y utilizando para ello equipos comerciales accesibles y de bajo coste. Se consigue disminuir 4 veces el tiempo de la clasificación manual y no es sensible a la variedad de tomate clasificado. Este sistema facilita los procesos de estandarización y control de calidad, permitiendo aumentar la competitividad de las explotaciones agrícolas de tomate, repercutiendo positivamente en s...

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“…Hassan Asadollahi, Morteza Sabery Kamarposhty, Mir Majid Teymoori [7] have presented in their paper the tomato classification by images processing. So this paper does allow us to look towards the classification of images of crops.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Method of Image Segmentation for Harvest Time Identification

Bhatnagar¹,

Singh²

2014

IJCA

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The aim of development and advancements in every research and technology is to provide easy solutions to any user's problems including the day to day ones. One problem that a farmer might face is to identify whether his crop is ready for harvesting or not. Hence the efforts are taken to figure out an easy solution for a farmer where he can use his cellular phone to identify whether his crop is ready for harvesting or not. This he can do by taking picture of his crop through the digital camera of his cellular phone. Therefore image processing plays an active role to figure out the solution to this problem. This paper proposes image segmentation of the image of a crop such as tomato to identify whether the crop is ripe enough or not. The feature that is extracted for image segmentation is moment. The classification technique used for segmentation is K-means clustering algorithm. The values for the segmented image viz. Mismatching Rate, Misclassification Rate, PSNR and MSE are then calculated Then to identify how much the system is robust noise is inserted in the image and then again the image is segmented and the above mentioned values are calculated. Then the image is denoised and again segmentation of the image is performed and the same process is followed. This paper presents the above mentioned process and the system produces equivalent results in all the three cases. The research work presents here the result of the image segmentation done on the image of a tomato using MATLAB version 7.10.

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Classification and Evaluation of Tomato Images Using Several Classifier

Cited by 8 publications

References 7 publications

A cost effective tomato maturity grading system using image processing for farmers

A cost effective tomato maturity grading system using image processing for farmers

High speed intelligent classifier of tomatoes by colour, size and weight

An Efficient Method of Image Segmentation for Harvest Time Identification

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