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.

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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

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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

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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 of Computer Vision System  Quality of images plays a vital role in outputs.  As agricultural products are diverse in shape and size, there is a need to develop separate algorithms for each one of them.  Artificial lighting is needed if the operation is carried out in dim or night conditions [15-18].

2.2 Application of Soft Computing in Computer Vision

Soft Computing is encompassing of seminal theories that include Fuzzy Logic, Genetic Algorithms, Evolutionary Computation, and Neural Networks. These techniques' ability to integrate inaccurate and incomplete information into modelling very complex systems makes them a helpful tool in image processing. They can be used for various image-processing applications - edge detection, segmentation, pattern recognition, object recognition, interpretation; Image enhancement: filtering, noise removal, enhancement, restoration; Image reconstruction: image similarity identification and retrieval.

2.3 Chilli as an Agricultural Produce

Chillies have been recognized in Peru since prehistoric times. In 1493, Columbus brought Chilli seed to Spain and later were spread to Europe. In 1584, capsicum was brought to India by Portuguese. Chillies were then grown along with the main crop to safeguard the main crop from any bird harm [19].

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International Journal of Advanced Science and Technology Vol. 29, No. 6, (2020), pp. 8480-8490 2.3.1 Uses of Chillies 1. Kitchens: Chillies are essential and the most significant ingredient in many distinct cuisines around the globe. Indian Chilli is regarded world-renowned for two significant business characteristics— its level of colour and pungency. Levels of colour and pungency differ depending on Chillies varieties and characteristics. 2. Pharmaceutical Industry: Chilli's pungency is due to the alkaloid (naturally occurring chemical compounds that mostly contain fundamental nitrogen atoms) ‗capsaicin‘ in the pericarp and placenta. The pharmaceutical industry uses pungent kinds of Chillies. Capsaicin is a strong and stable alkaloid that in solutions of ten components per million can be identified by human taste buds. Capsicum ‗oleoresin‘ finds used in Pharma as a powerful carminative and stimulant. It is used as anti-irritant medicine. 3. Hotel and Bakery Industry: Chilli for its potent flavor and aroma is extensively used in the hotel sector, Namkeens, Readymade Masalas, Pan Masala, Tobacco and Sauces, Oleoresins discover broad application in a variety of sectors. 4. Colouring agent: ‗Capsanthin‘ is a red, dark liquid that is soluble in oil. Chilli red pigment has a high level of safety, heat resistance, light resistance and environmental acidity resistance. The product is therefore commonly used in aquatic products, meat, cakes, salads, canned food, beverages, cosmetics, medications, and so on as the coloring agent.

2.3.2 Botanical Description of Chilli Chilli is a fruit from the ‗Capsicum‘ genus of the ‗Solanaceae‘ family, which includes tomatoes and potatoes as well. The word Capsicum has Greek origin. The term ―Kapsimo‖ means, ―to bite‖. Hunziker-Monotypic Tubocapsicum, Pseudoacnistus and Capsicum are three genus of Capsicum [19].

2.3.3 Importance of Chilli as an Agricultural Crop 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 [19]. Chilli's export accounts for 48% in terms of amount and 28% in terms of the value of India's complete spice export. Today, India is the primary caterer of global demand for 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 [19]. The marketing channels in India are displayed in Fig. 1. The above discussion shows that Chilli is one of the principal agricultural products of India as it is both consumed internally and also exported. As per the report on Post Harvest Profile of Chilli published by the Department of Agriculture, Ministry of Agriculture, Government- Financial problems, lack of marketing information, Inadequate cold storage & other facilities, lack of training to the producer regarding harvest, transportation and marketing of Chillies are the major constraints. Due to the insufficient volume and capability of processing units, the surplus output is sold at a distressing rate during peak season, or even lost at farm level. Grading of Chillies based on various quality parameters, will fetch higher prices and satisfy the customer need for quality products. Most markets, however, lag in offering grading service [19].

The food supply chain represents the movement of food alongside peasant households, processing facilities, distribution centers, wholesalers, retailers and consumers. The food supply chain generally consists of five connections (shown in Fig. 2): the product supply link material, the production-processing link, the packaging, storage and transport link, the sales link and the customer-expending link with each link containing associated sub- links and the various organization carriers. The supply chain is a networked structure that

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International Journal of Advanced Science and Technology Vol. 29, No. 6, (2020), pp. 8480-8490 consists of physical flow, data, finance, technical, standardized, safety, and value-added links [6-7].

Figure 1. Chilli - Marketing Channels in India [6]

Figure 2. Food Supply chain basic structure [6]

Consequently, adequate automated processes are needed to assist in the scientific and large-scale grading of Chillies at all stages of the food supply chain. The report on postharvest profile of chilli published by the Department of Agriculture, Ministry of Agriculture, Government of India emphasized the need to modernize their quality grading in order to cater to the customer needs-driven market. Visits to agricultural universities and agro-based industries emphasized that there is a need to explore the development of a Computer Vision Framework for automatic post-harvest quality grading of dry Chilli. Hence, the study emphasizes on the applications of Computer-vision for Quality Grading of Food Products.

2.3.4 Physical Chilli Grading Machines Some of the Chilli Sorting Mechanism that are available in the market include Photodiode scanners, basic colour sorters and mechanical sorters. Photodiode Scanner: Federico Hahn's sorter [8] classifies Chilli by three separate widths. The conveyor used suckers for each chilli. A laser line generator photodiode scanner was used to identify incoming radiation. A radiometer recognized necrosis-presenting chillies and removed it to enhance product quality. Necrosis detection and classification of widths were 96.3% and 87% respectively. On-line necrosis measurements were 85% precise using only 550 nm comparative reflectance.

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International Journal of Advanced Science and Technology Vol. 29, No. 6, (2020), pp. 8480-8490

Figure 3. Gap Belt Separator [8]

Gap Belt Sorter: In the M-TEC engineers conceptualized gap-belt sorter, a tilted belt would transfer harvested material to a second belt. Material would travel parallel to belt direction. Pods would fall through a gap between the tilted belt and the trash belt, sticking longer than the gap between the trash belts (as shown in Fig. 3). The gap belt requires being likewise oriented [8]. Colour sorter to remove sticks from harvested red chilli: All colour sorters tested use Pulsed Light Emitting Diode (LED) technology to determine the colour of objects falling from a belt's edge. Pulsed LED technology flashes distinct wavelength LEDs and gets a reflection of each wavelength off the scanned material. Colors are distinguished based on ratios between wavelengths. In each device, the processor sends a signal to a solenoid air valve to open the valve, actuating a plastic reject finger. The reject finger diverts the target material from its natural trajectory into a distinct bin or conveyor belt [9].

Figure 4. Chilli Colour Sorting Machine [10]

Chilli Color Sorter shown in Fig. 4 is a tool capable of sorting Chilli based images captured by a high-resolution CCD image sensor. This unit performs only fundamental color sorting based on threshold value algorithms. It ignores the length, size and other parameters [10].

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International Journal of Advanced Science and Technology Vol. 29, No. 6, (2020), pp. 8480-8490 3. Materials and Methods

3.1. Computer-vision for Quality Grading of Food Products

Quality grading is necessary for making any food product marketable. Quality parameters of food products include both Visual and Olfactory factors. Quality grading primarily consists of a classification of the food products by classifying based on various quality parameters. This idea is to see that each of the values of the quality parameters is graded based on the well-defined threshold limits [3] [5].

3.2 Schematic for building an Intelligent Chilli Sorter

The Fig. 6 and Fig. 7 shows the graphical representation of conceptual model of Intelligent Chilli Sorter. In this, the machine part 1 is the Hopper through which the Chilli would be fed. This Hopper is connected to Part 2 which is the feeder tube that passes the Chillies to Part 3-the Stage-I conveyer belt. When the Chillies pass through the Stage-1 Conveyor belt, the thermal image is captured and the same is used to determine the moisture content. If the Chilli is not of the pre-requisite quality, it is popped out using 3 Bar pressure nozzle. Fig. 6 presents the detailed view of Camera and Nozzle Mounts of Conceptual Model of Intelligent Chilli Sorter Machine. If the Chilli passes the moisture test it moves to the Stage-II conveyor belt where 2D-Digital image is captured for further quality analysis like Morphology, Colour and Pungency. If any of the quality parameters are not of the required quality as desired by the Customer the Chilli is popped out at the end.

3.1.2 Multi-Stage Decision making Intelligent Chilli Sorter

The Fig. 5 shows the flow chart of the process of Multi-stage Decision making and Fig. 6 shows the Schematic for Intelligent Chilli Sorter. The proposed Chilli sorter works as follows:

1. Initially the image of a chilli is captured using the Thermal Camera and Alpha value is determined. The chilli is either accepted or rejected based on the acceptable value of Alpha. A thermal camera can be used to carry out a non- destructive moisture analysis of Chilli quality. 2. The 2D-Digital image of the Chilli that has passed the moisture content is taken and the R-Value of RGB value is extracted. The same is used to predict the IC (International Colour Units)/ASTA (The American Spice Trade Association) value. If the IC value is below the required IC value it is pushed to the rejected lot else further processing is carried out. 3. The Morphological features of Chillies that have passed the IC value requirements are extracted using the one of the methods proposed by Dasharathraj et al., If the Chilli does not belong the required grade the same is sent to the rejected lot [20] 4. Once the Chilli passes the Morphological grade test the Girth of the Chilli is used to grade the Chilli based on Pungency. If the Chilli does not belong to the required grade of Pungency, it is sent to the rejected lot else it is sent to the accepted lot. 5. If a Chilli passes all the grades this means that it is graded according to Morphology, Moisture, Colour and Pungency.

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International Journal of Advanced Science and Technology Vol. 29, No. 6, (2020), pp. 8480-8490

Figure 5. The process for Quality Grading of Chilli

Figure 6. Schematic of Intelligent Chilli Sorter Machine

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International Journal of Advanced Science and Technology Vol. 29, No. 6, (2020), pp. 8480-8490

Figure 7. Schematic for Intelligent Chilli Sorter

The Fig. 7 shows the schematic of intelligent chilli sorter machine, Fig. 8 shows detailed view of camera, and nozzle mounts of schematic of intelligent chili sorter used in the chilli sorting process, which help to segregate the chillies and grade them at a faster rate at higher accuracy.

Figure 8. Detailed view of Camera and Nozzle Mounts of Schematic of Intelligent Chili Sorter

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International Journal of Advanced Science and Technology Vol. 29, No. 6, (2020), pp. 8480-8490 5. Conclusion

The paper presents a Schematic for an Intelligent Chilli Sorter for postharvest quality grading of chilli, taking into consideration the commercial value of chilli and results of the literature survey. The paper provides a framework for the application of Soft Computing-based Computer Vision System for automatic post-harvest quality grading of dry red chillies. 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.

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