An Automated Machine Vision Based System for Fruit Sorting and Grading Chandra Sekhar Nandi University Institute of Technology. The University of Burdwan Burdwan, India chandrasekharnandi@gmail.com Bipan Tudu IEE Department Jadavpur University Kolkata, India bip_123@rediffmail.com Chiranjib Koley Electrical Engineering Department National Institute of Engineering Durgapur, India chiranjib@ieee.org Abstract—The paper presents a computer vision based system for automatic grading and sorting of agricultural products like Mango (Mangifera indica L.) based on maturity level. The application of machine vision based system, aimed to replace manual based technique for grading and sorting of fruit. The manual inspection poses problems in maintaining consistency in grading and uniformity in sorting. To speed up the process as well as maintain the consistency, uniformity and accuracy, a prototype computer vision based automatic mango grading and sorting system was developed. The automated system collect video image from the CCD camera placed on the top of a conveyer belt carrying mangoes, then it process the images in order to collects several relevant features which are sensitive to the maturity level of the mango. Finally the parameters of the individual classes are estimated using Gaussian Mixture Model for automatic grading and sorting. Keywords—machine vision; fruit grading and sorting; video image; maturity prediction; Gaussian mixture model I. INTRODUCTION Automated grading and sorting of agricultural products are getting special interest because of increased demand in different quality food with relative affordable prices by the different group of customers belongs to different living standards. Thus fruit produced in the garden are sorted according to quality and maturity level and then transported to different standard markets at different distances based on the quality and maturity level. Sorting of fruits according to maturity level is most important in deciding the market it can be sent on the basis of transportation delay. In present common scenario, sorting and grading of fruit according to maturity level are performed manually before transportation. This manual sorting by visual inspection is labour intensive, time consuming and suffers from the problem of inconsistency and inaccuracy in judgement by different human. Which creates a demand for low cost exponential reduction in the price of camera and computational facility adds an opportunity to apply machine vision based system to assess this problem. The manual sorting of fruits replaced by machine vision with the advantages of high accuracy, precision and processing speed and more over non-contact detection is an inevitable trend of the development of automatic sorting and grading systems [1]. The exploration and development of some fundamental theories and methods of machine vision for pear quality detection and sorting operations has been accelerate the application of new techniques to the estimation of agricultural products’ quality [2]. Many color vision systems have been developed for agricultural grading applications. These applications include direct color mapping system to evaluate the quality of tomatoes and dates [3], automated inspection of golden delicious apples using color computer vision [4]. In recent years, machine vision based systems has been used in many applications requiring visual inspection. As examples, a color vision system for peach grading [5], computer vision based date fruit grading system [6], machine vision for color inspection of potatoes and apples [7], and sorting of bell peppers using machine vision [8]. Some machine vision systems are also designed specifically for factory automation tasks such as intelligent system for packing 2-D irregular shapes [9], versatile online visual inspections [10], [11], automated planning and optimization of lumber production using machine vision and computer tomography [12], camera image contrast enhancement for surveillance and inspection tasks [13], patterned texture material inspection [14], and vision based closed-loop online process control in manufacturing applications [15]. With this back ground, the proposed technique applies machine vision based system to predict the maturity level of mango from its RGB image frame, collected with the help of a CCD camera. The materials and method are discussed in Section II. Details preprocessing of image is discussed in Section III. Different feature extraction methods are discussed in Section IV. The theory of GMM is discussed in Section V, and the result and discussion in Section VI. We summarize our work and conclude this paper in Section VII. II. MATERIALS AND METHOD A. Sample Collection For the experimental works total 600 number of unsorted mangoes of four varieties locally termed as “Kumrapali” (KU),“Sori” (SO),“Langra” (LA) and “Himsagar” (HI) were collected from three gardens, located at different places of West Bengal, India. Collection of mangoes were performed in three batches with an interval of one week in between batches and in each batch 200 numbers of mango were collected, having 50 numbers of each variety i.e. KU, SO, LA and HI. Steps were taken to ensure randomness in mango collection process from the gardens in each batch. After collection of mangoes each mango were tagged with some unique number generated on the basis of variety, name of the origin garden, batch number and serial number etc. Three independent human experts work in the relevant field were selected for manual prediction of maturity. 2012 Sixth International Conference on Sensing Technology 978-1-4577-0167-2/12/$26.00 ©2012 IEEE 195