Kavindra R. Jain

Non-destructive quality evaluation in spice industry with specific reference To Cuminum cyminum L (cumin) seeds

The Indian spice industry by and large is primitive yet. Screen cleaner and dust removing are the only operations which are being done alone in the industry. Quality assessment of spices is a very big challenge since time immemorial. In addition to inherent and hygienic features quality depends on its physical appearance, moisture content, composition which may be reflected by taste and smell too. Human sensory panel generally assess quality and such process is time consuming, unreliable and non reproducible. There is a need for some non invasive quality testing methodologies. This paper proposes a new method for counting the number of Cuminum cyminum L (cumin seeds)with long pedestals as well as foreign elements using machine vision non destructive technique based on combined measurements.

Image morphological operation based quality analysis of coriander seed (Coriandrum satavum L)

The paper presents a solution for quality evaluation and grading of spice food industry using computer vision and image processing. In this paper basic problem of Indian spice industry for quality assessment is defined which is traditionally done manually by human inspector. This method is time consuming as well as costly. With the help of proposed method for solution of quality assessment via computer vision, image analysis and processing a fast and accurate quality evaluation is achieved. In this paper we propose a method for counting the number of Coriandrum sativum L (coriander seeds) with long pedestals as well as foreign elements using image processing with a high degree of quality and then quantify the quality of the coriander seeds.

Non-destructive Quality Analysis of Indian Basmati Oryza Sativa SSP Indica (Rice) Using Image Processing

The Agricultural industry on the whole is ancient so far. Quality assessment of grains is a very big challenge since time immemorial. The paper presents a solution for quality evaluation and grading of Rice industry using computer vision and image processing. In this paper basic problem of rice industry for quality assessment is defined which is traditionally done manually by human inspector. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique. With the help of proposed method for solution of quality assessment via computer vision, image analysis and processing there is a high degree of quality achieved as compared to human vision inspection. This paper proposes a new method for counting the number of Oryza sativa L (rice seeds) with long seeds as well as small seeds using image processing with a high degree of quality and then quantify the same for the rice seeds based on combined measurements.

Unified Approach in Food Quality Evaluation Using Machine Vision

The paper presents a unified approach for quality evaluation of food using image processing and machine vision. In this paper basic tool is combination of computer and machine vision for image analysis and processing through which fast and accurate quality is achieved that too with the help of non-destructive method. Machine vision in food has broadened its range of applications from grains, cereals, fruits to vegetables including processed products as well as spices in which there is a high degree of quality achieved as compared to human vision inspection. In this paper we quantify the qualities of various food products and figure out features which are directly or inversely affect the quality of the food product. Based on these features a generalized formula of quality is proposed to be used for quality evaluation of any type of food product.

Non-Destructive Quality Evaluation in Spice Industry with Specific Reference to Cuminum Cyminum L (Cumin) Seeds

The Indian spice industry by and large is primitive yet. Screen cleaner and dust removing are the only operations which are being done alone in the industry. Quality assessment of spices is a very big challenge since time immemorial. In addition to inherent and hygienic features quality depends on its physical appearance, moisture content, composition which may be reflected by taste and smell too. Human sensory panel generally assess quality and such process is time consuming, unreliable and non reproducible. There is a need for some non invasive quality testing methodologies. This paper proposes a new method for counting the number of Cuminum cyminum L (cumin seeds)with long pedestals as well as foreign elements using machine vision non destructive technique based on combined measurements.

Non-destructive Quality Analysis of Kamod Oryza Sativa SSP Indica (Indian Rice) Using Machine Learning Technique

Rice is one of the most important cereal grains. The paper presents a solution for quality evaluation and grading of Krishna Kamod rice using image processing and soft computing technique. In this paper basic problem of rice industry for quality assessment is defined which is traditionally done manually by human inspector. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique. The proposed method for quality assessment of INDIAN KAMOD ORYZA SATIVA SSP INDICA (Krishna Kamod Rice) using image processing and multi-layer feed forward neural network technique which achieves high degree of quality than human vision inspection. The proposed algorithm based on morphological features is developed for counting the number of Krishna Kamod rice seeds with long seeds as well as small seeds. A trained multi-layer feed forward neural network based classifier is developed for identification of unknown rice seed quality.

Development of a Classification System for Quality Evaluation of Oryza Sativa L. (Rice) Using Computer Vision

Carrying out effective and sustainable agriculture product has become an important issue in recent years. Agricultural production has to keep up with an ever-increasing population. A key to this is the usage of modern techniques (for precision agriculture) to take advantage of the quality in the market. The paper reviews various quality evaluation and grading techniques of Oryza Sativa L. (rice) in food industry using computer vision and image processing. In this paper basic problem of rice industry for quality assessment is defined which is traditionally done manually by human inspector. Computer Vision provides one alternative for an automated, non-destructive and cost-effective technique. In this paper we quantify the qualities of various rice varieties in Asian subcontinent and figure out features which directly or indirectly affect the quality of the rice. Based on these features a generalized approach of quality is proposed to be used for quality evaluation of any type of rice variety.

Non-destructive Quality Evaluation Technique for Processed Phyllanthus Emblica(Gooseberry) Using Image Processing

This paper proposes non-destructive quality evaluation method to categorize a processed phyllanthus emblica (gooseberry) using image processing by color and texture features. Russia is one of the most important gooseberry producers in North Asia, than Germany, Poland, U.K, India etc, but fruit sorting in some area is still done by hand which is tedious and inaccurate. Thus, the need exists for improvement of efficiency and accuracy of this fruit quality assessment that can meet the demands of international markets. Low-cost and non-destructive technologies capable of sorting processed gooseberry according to their properties would help to promote the gooseberry export industries. This paper propose the method of colorization and extracting value parameters, by this parameters the detection of browning or affected part and identification of the uniform shape and size. This differentiate the quality of processed gooseberries.

Quality Evaluation of Hydrothermal Treated Quicker Cooking Scented Rice by Quantification of Quickness of Cooking Time and Mechanical Strength Using Machine Vision

Rice (Oryza sativa L.) together with wheat and maize is one of the most important cereal crops of the world. Hydrothermal treatment is used to be given to the scented rice to produce quicker cooking rice. The quickness in terms of time for cooking for scented cooked rice is then evaluated on the basis of fissures so produced in the form of rings using microscope. In this paper we are proposing a machine vision system and a novel technique for quality evaluation of Hydrothermal treated quicker cooking scented rice by quantification of quickness of cooking time and mechanical strength evaluation based on image processing techniques.

An Automatic Segmentation Approach Towards the Objectification of Cyst Diagnosis in Periapical Dental Radiograph

The crucial part of image segmentation is the proper selection of initial contour to start with the efficient process. The main reason for such kind of segmentation is to reduce the human interaction and moreover to have more accurate results. In this paper we trying to objectify the cyst diagnosis problem in periapical images with the help of automatic segmentation. We have utilized the internal and external energy of image forces that pull it toward features such as lines and edges, confining them precisely. Scale space continuation can be used to develop the catch area encompassing a component.