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Journal Paper Format International Journal of Advanced Science and Technology Vol. 29, No. 6, (2020), pp. 8480-8490 Schematic for a Computer Vision Framework for Post-Harvest Quality Grading of Dry Red Chilies Dasharathraj K Shetty1, Dinesh U Acharya2*, Nithesh Naik3 1Humanities and Management, Manipal Institute of Technology, Manipal, Manipal Academy of Higher Education, Manipal 2Computer Science and Engineering. Manipal Institute of Technology, Manipal, Manipal Academy of Higher Education, Manipal 3Mechanical and Manufacturing Engineering. Manipal Institute of Technology, Manipal, Manipal Academy of Higher Education, Manipal *Corresponding author: [email protected] Abstract The automatic sorting of the agricultural and food products is the need of the current era as there is a higher emphasis on quality. Food and Agricultural Organization (FAO) statistics say that 2959 thousand tonnes of Chilli are raised in the world over an area of 1832 thousand hectares. Chilli's export accounts for 48% in terms of amount and 28% in terms of the value of India's complete spice export. India is the primary caterer of global demand for Dry Red Chillies. It exports powder, dried Chilli, Pickled Chillies and Chilli oleoresins. Countries such as The United States of America, The United Kingdom, Germany and Sweden use Chilli to produce large-scale oleoresins and extracts. Considering the commercial value of Chilli and results of the literature survey built a strong case to undertake the automation of Postharvest quality grading of Chilli. The study provides a framework for the application of Soft Computing-based Computer Vision System for Automatic Post Harvest Quality Grading of Dry Capsicum Species (Red Chilli). Given the disadvantages of manual methods and optoelectronic methods, which process only a single dimension of data, this study has provided a novel approach by using Computer Vision for Chilli Quality Grading. Morphological Features, Moisture Content, Colour and Pungency are the four quality parameters based on which the Chillies can be sorted. The study presents Schematic for Intelligent Chilli Sorter that proposes to use Artificial Neural Network for the prediction of Capsanthin content determination (ASTA/IC Colour Units); use thermal images to determine the moisture content of Chillies; use the Girth of Chillies extracted from the images to determine the Pungency of the Chillies. This paper presents a Schematic of Computer Vision Framework for Post-Harvest Quality Grading of Dry Red Chilles that uses multistage decision-making. Keywords: We would like to encourage you to list your keywords in this section 1. Introduction Agricultural production levels have risen, as hybrid cultivations are introduced to satisfy the ever-rising population's growing food demand [1]. This also includes the implementation of automation systems in the agricultural sector from food crop processing to supply. The latest era of intelligent technology allows more and more food to be generated with fewer workers [2]. According to various ecological and socioeconomic conditions, technical approaches for improved agriculture are not at large- scale for everyone, and so such technologies must then be produced at lower costs for mass adoptions, 9which in turn improves the local farmers' and other farming industries 8480 ISSN: 2005-4238 IJAST Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 6, (2020), pp. 8480-8490 [3,4]. The segregation and sorting of fruits and vegetables are unproductive for human labour among many automation services in agriculture sectors. With emerging technology such as artificial vision, image recognition, etc., this process can be automated. The main aim of the paper is to develop an automated system that uses the machine vision to sort chilli at a rate that the producers can afford. Some fruits and plants such as chillies, orange, citrus fruits, eggplants, tomatoes, etc., can be seen in maturity or maturity by seeing their colour. When using a machine vision system, this variation of colour difference can be quickly determined, and the fruit and vegetable is separated when correct conveyor arrangements and sorting devices. Fruit and vegetables can be checked and sorted very tedious [5]. Inspectors manually identify fruits and vegetables in countries where there is inexpensive labour, but discrepancies in visual and practice are ignored. Experienced persons manually sort chilli, and a computer has been designed to identify chillies by taking into account their ranking decision automatically [6]. However, for the proposed system, the colour of the chillies and the surface area are prioritized instead of other parameters such as size, width, etc. 2. Literature In many food varieties, chilli is a common ingredient. India has about 1551 kg/hectare average chilli production. Pravin Jagtap addresses the following figures in his article mentions the statistics of India, with Andhra Pradesh (49%), Karnataka (15%), Maharashtra (6%) and Tamilnadu (3%) are the country's largest chilli producing states [7]. After harvesting, the chillies are classified based on colour, whilst green chillies are sold right on the market, while red chillies are further processed and dried which can be used to make chilli powder [8]. This is usually done manually in farming fields. The method of sorting the crop by the human being according to unnecessary, unproductive visual criteria poses several challenges [9]. The process of sorting different fruit and vegetables such as chillies, tomatoes or lemons is automated by various methods and technologies such as machine vision, image processing, etc. F. Hahn developed a system to determine the chillies using a camera system based on length/width. The light is structured and produced by a group of lasers to fall on the chills, and then the light rays are captured by a camera for the monochromatic charging device (CCD). The image is then analyzed to assess the duration and width of the chillies used to detect them [10]. By using machine vision tools, Arjenakiand et al. sorted the tomatoes using the system was sorted using a CCD camera with Sony polarizing films, Atmega8 microcontroller, 2.8 Mhz computer. Visual Basic 2008 developed the software program for this trial system. The methods in the sorting process include obtaining the tomato images from the transmitter belt and processing them for the tomatoes with HSI and RGB threshold spaces. Tomatoes are unripe and completely mature based on their form, maturity, size and average components of colour. [11]. S. Laykin et al. created an advanced image-processing algorithm to create a similar tomato classification/sorting system. They used three cameras (CCD) for the image capture purpose of their project with a halogen spotlight setup. MATLAB® has been used to develop the algorithm and software to identify the tomatoes. The tomatoes were ranked in ten classes and four major classes such as red, pink, green and rejected. The RGB and HSV rooms in the acquired images are used in this method and further processing images such as rim identification, colour thresholding in the graphic, roundness measurement of the tomatoes etc. are graded accordingly [12]. Pla et al. developed a machine vision system to sort industrial fruit and vegetables. They have a machine vision system with sensors like weight sensors and three cameras where one is a CCD camera, and the other two are monochrome infrared and ultraviolet CCD cameras. The camera detects the 8481 ISSN: 2005-4238 IJAST Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 6, (2020), pp. 8480-8490 presence of fruit or vegetables in the transport system, the ultraviolet filtered camera detects defects, and the colour camera is applied to identify fruit or vegetables maturity and other factors during the sorting processes. Data from the weight sensor, three cameras is communicated using the CAN protocol and transmits the output from the controller to the actuators. The system can sort 15 fruits or vegetables per second [13]. 2.1 Need for Computer Vision in Post-Harvest Quality Grading Quality control deals with maintaining parameters within acceptable buyer tolerance limits. This helps minimize the vendor's price [14]. Quality control relates to the use of different techniques to obtain the wholesome product, including technological, physical, chemical, microbiological, dietary and sensory parameters. Post-Harvest processing in agriculture involves grading and selection processes, which are usually done manually or using mechanical devices, which are largely based on the appearance/morphological features of the produce. Hence, post-harvest processing is one of the potential targets for image processing. Quality grading by appearance is often ill defined and subjective, and therefore, it is challenging to implement with predefined algorithms. Trainable pattern classifiers that can be taught what are good and bad may be particularly suitable for this type of application. A generic model of a Computer Vision System consists of Illumination, Image Capturing Device, Image Capturing Board (Frame Grabber/Digitizer), Computer Hardware and Software. 2.1.1 Benefits of Computer Vision-based Systems Acquiring agricultural data manually is either cumbersome or impossible, whereas CV archives this with higher speed. Provides rapid alternative means for measuring quality consistently. Information is attained in a non-destructive and non-disturbing manner. 2.1.2 Drawbacks
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