Research.pdf

ACKNOWLEDGEMENT This project became a reality with the help and support of kind individuals who stayed through the ups and downs in every phases and gave encouragement and ideas. The developers would like to extend sincere thanks to following people: Foremost, to GOD Almighty for the wisdom and knowledge He bestowed upon them, the strengths, peace of mind, good health and the courage and perseverance to pursue the study and satisfactorily finished it amidst the hardships encountered. The developers would also like to extend their deepest appreciation and thanks to their adviser Mrs. Dhally A. Ilisan. Without her full support, guidance, enthusiasm, encouragements, patience, proficiencies shared and continues belief to the developers abilities, this study would not be overcome and would hardly have been completed. To the developer’s technical expert, Dr. Virgie P. Ugay for the capstone idea, title and also for imparting her knowledge of the field which is new to the developers. For the guidance and advise given that helped the developers in crafting the logic and other important information needed in the course of completing the project. The developers are grateful for all the things you have done. To the project panelists, Mrs. Editha Hebron, Mr. Archie Cenas and Ms. Mishiill D. Cempron for sharing their expertise, time, giving the developers ideas and insights that helped them improve their application and for giving

encouragements all throughout the development of the project, their sincerest appreciation. To the developers’ greatest source of strength and inspiration, their families who gave outmost support physically, emotionally, mentally and financially, showing unconditional love and encouragements throughout the process of completing the project. Lastly, the developers would like to thank their classmates and friends, a new found family, who have been with them in the past few years. Thank you for the love, prayers, time, encouragements and support may it be morally, physically or academically, without their help the developers could hardly surpass all the trials and hardships in the course of getting this degree.

EXCECUTIVE SUMMARY Digimango: A Digital Image Processing on Anthracnose Severity of Mango is an offline mobile Android-based application that analyzes and produce rating results on severity of anthracnose disease found in mango fruit. It is a tool that evaluates quantitatively the development of spot-like lesion on the surface of the said fruit. The purpose of Digimango is to help the researchers

and

plant

technicians

in

post-harvest

fertilizer

product

experimentations in identifying and objectively rating the severity of anthracnose disease in individual fruit with less time and effort. The developers used Android IDE in developing the application and OpenCV as the library. The application requires at least 8mp (megapixels) camera with 2GB/16GB RAM. Mobile Application Development Lifecycle (MADL) is used as the projects methodology. With this methodology, the developers were able to analyze, structure, plan, design, develop and implement the application thoroughly and met all the applications objectives. The developers concluded that there are things that need to be considered when using the application. Illumination, flash must be turned on and capture must be done indoor with the presence of fluorescent light. Background. It must be a plain white paper or cloth with 22x28 inches size. Distance. The background size is provided thus the user must capture from one edge to another. The sample is a three dimensional object so the capture will be done four times to evaluate all the samples part.

CHAPTER I INTRODUCTION Project Context Mango (Mangifera indica) is a tree of the Anacardiacease family, whose fruit has been consumed for thousands of years [7]. It is a famous South Asian fruit, that has been a popular addition to "tropical" and refreshing fruit platters for decades and has lots of varieties [12]. The ´Carabao´ variety, endemic to the Philippines, is considered to be one of the finest and sweetest variety in the world and is the leading the mango variety exports [22]. The mango business is mainly the famous segment of horticulture and its development has been fast and remarkable. In fact, in 2016 Philippines shipped 13,928.714 metric tons (MT) of fresh mangoes worth $12.57 million, and aside from fresh mangoes, 363.28 MT of dried mangoes, valued at $3.731 million, was shipped to European counties on the same year [3]. On the other hand, in spite of the inspiring growth of this sector, a number of issues need to be addressed, of which the productivity of mango trees in the region is principal. Presently, mango suffers from several diseases at all stages of its life that result to lower yield and income. The most common and easily identified disease by mango fruit buyers is the anthracnose. Mango anthracnose is caused by the fungus Colletotrichum gloeosporioides var minor (also known by the name of its perfect stage Glomerella cingulata var minor). Spore production by this fungus is favoured by wet or humid weather. The dispersal of these spores is particularly favoured by

rain and wind [9]. This disease leaves the fruit’s epidermis with black spots. Thus the buyers easily get noticed that the fruit is not suitable for consumption. Fresh mango fruits from the country hardly make it to the international market due to anthracnose infection. It is a major disease limiting fruit production in all countries where mangoes are grown, especially where high humidity prevails during the cropping season. In Southwest Nigeria, Oneyeani et al. (2015) a survey in mango production belt was conducted in four locations and has found out that 60% of the trees surveyed are infected with anthracnose and 34% of fruits produced were severely infected [2]. Postharvest mangoes are examined through plant pathologist methods such as disease intensity that is basically done through the use of the naked eye [14]. In the Philippines, visual ‘hedonic’ scales (subjective testing carried out by trained evaluators) is still practiced by both national and local farmers including plant pathologist. This kind of evaluation is qualitative and subjective, as it depends on the experience of the evaluators. This practice of disease measurement is the standard scaling used by researchers all over the world. Data that is being gathered through this evaluation are used for researches for further study of the appropriate medicines and fertilizers to be used to control anthracnose disease. With this, the group proposed DigiMango, an application that will measure the percentage of severity of the anthracnose of a mango fruit.

Purpose and Description Digital Image Processing on Anthracnose Severity of Mango (DigiMango) is an Android application that uses a smart phone camera to analyze and produce rating results of the severity of anthracnose disease in a mango fruit. DigiMango captures four (4) sides of a mango fruit and measures the percentage of the total surface area where the disease is visible. DigiMango is a tool that is used to evaluate quantitatively the development of spot-like lesions on the surface of mango fruit. The application used evaluation based on scale, assessment from (mild infection to severe infection) based on the evaluation of percentages of the area affected in the sampled fruits: 0% -5.9 % (no infection), 6% - 50.9% (average infection), 60% - 100% (severe infection) depending on the scope of the area affected by anthracnose lesions. Every transaction is considered as one project. Every project has a minimum of one (1) and a maximum of ten (10) mangoes to be captured at the same time. The Application will show results of its individual rating and the evaluation of anthracnose lesions in each sample. Additionally, the user can add more mangoes in one project, in which after the result of one transaction is shown, an option to add more mangoes is presented where the user can capture another set of mangoes given that the result of the first transaction and the latest transaction will be presented as one. The user in the application can either save or delete the said results. With this application, the group aimed to help the researchers and plant technicians in post-harvest fertilizer product experimentations, gathering data.

This served as basis and guide to what they must do to improve the quality of fruits in the next yield. Objectives of the Study This study aimed to develop Digital Image Processing on Anthracnose Severity of Mango (DigiMango). Specifically, this study aimed to develop project modules that will perform the following functions. 1. Evaluate the percentage of anthracnose lesions in a mango fruit. 2. Calculate the rating of anthracnose disease on mango fruit at the maximum of ten (10) samples in one transaction. 3. Store the past ratings of analyzed samples of mangoes. Scope and Limitations of the Project The Digital Image Processing on Anthracnose Severity of Mango (DigiMango) was developed for the farmers and growers of mangoes and for the researchers that specialized the study of the said fruit. This Application was conceptualized to aid in measuring the severity of anthracnose disease in postharvest mangoes quantitatively by the use of image processing. The project is an offline android-based application that is used for capturing, processing and analyzing digital images in evaluating quantitatively a mango’s anthracnose using the smartphone’s built-in camera. The application includes presentation of the scales and the disease’s severity rating with the

added samples evaluation. It has options to either save the result for future use or delete when found unnecessary. An option to have an evaluation of bulk and individual mango is available, but only a maximum of ten (10) mangoes can be catered for bulk entries. Thus, mangoes captured in bulk entries will be rated individually. The application generates the following evaluation result: the evaluation based on scale. It involves assessment scales from mild infection to severe infection based on the evaluation of percentages of the area affected in the sampled fruits 0% -5.9 % (no infection), 6% - 50.9% (average infection), 60% 100% (severe infection). It has an archive so that the user can view the saved results of the evaluations. The user of the application can also delete the said results. To acquire the accurate results, the following must be considered, (1) illumination must be considered carefully to attain desirable results in which the application must only be used indoor and that the user is obliged to turn on the phone’s flash; (2) the mangos that will be captured must be placed in a white colored background, such as a white fabric to avoid color miscalculations, the background’s size must be 22x28 inches (cartolina size);(3) while capturing the subject the frame must be from one edge of the background to the other edge, this is advised to further address the differences of smartphone cameras focal lengths; (4) the application will only scan a maximum number of ten (10) mango fruits; (5) the application is just a tool to measure the severity of the disease in the fruit and will not include any instructions on how to cure the said disease; (6) and lastly, the application is only limited for mango anthracnose evaluation

in agricultural purposes and any other fruits or things cannot be validated as needed. Furthermore, DIGIMANGO is an application that is dependent on images. That is why usage of high end smartphone cameras is greatly advised.

CHAPTER II REVIEW OF RELATED LITERATURE AND RELATED SYSTEM This chapter presented the related literature and studies gathered from other sources. This chapter helped to understand the projects that are relevant and similar to the present study. Related Literature Mango (Mangifera indica) is a juicy stone fruit that belongs to the family of Anacardiaceae in the order of Sapindales and is grown in many parts of the world, particularly in tropical countries [21]. It is also known as the “king of fruits” because it is the most popular fruit in tropical regions. It is the national fruit of India and Philippines, and the national tree of Bangladesh [15]. Mango is a perennial, branching, evergreen tree approximately 30–40 feet tall. Its fruit is large, fleshy drupe containing a laterally compressed stone housing the seed. Mango cultivars vary considerably in fruit size, color, shape, flavor, texture, and taste [8]. It is commercially the most important fruit crop of India, accounting for 54% of the total mango produced worldwide [29]. Mangoes are among the most traded fruits in the world. Currently, Asia is the largest producer of mangoes, with a global production of 72%, followed by Africa (17%), Latin America (10%), and the rest of the world (1%). Mango production has been growing over the years with India being the world’s largest producer of mangoes followed by China, Thailand, Indonesia, Pakistan, Mexico, Brazil, Bangladesh, Nigeria and the Philippines [18].

The Philippine mango industry has been thriving both in the local and international markets, with production as high as 783,225 metric tons combined for mangoes, mangosteens, and guavas in 2012, according to the Food and Agriculture Organization (FAO) of the United Nations [4]. Among the 23 barangays of Tagum City, Barangay Mankilam has the largest production of mangoes according to the official website of the City Government of Tagum. It has a production value of ₱136,875.00 [8]. Many physiological disorders in fruit affect both quality and storage life of mango in all growing regions of the world [25]. Mango crop prone to a number of diseases at all stages of its growth, from the plants in the nursery to the fruits in transit and storage. Almost every part viz. stem, branch, twig, root, leaf, petiole, flower and fruit are affected by various pathogens. These diseases manifest themselves as several kinds of rots, die back, mildew, necrosis, scab, blotch, stem bleeding, wilt, spots, canker, mould, malformation, etc. Some of these diseases is the reason of heavy loss and have become preventive factor in mango cultivation in some regions. Powdery mildew, anthracnose, die back, sooty mould, gummosis, malformation, blacktip and internal necrosis cause great loss [16]. Mangoes are susceptible to a range of pest and diseases that can significantly reduce commercial production and fruit quality [13]. The two most significant postharvest diseases of mangos grown throughout the tropics and subtropics are anthracnose caused by Colletotrichum gloeosporioides and stem-end rot caused by Dothiorella dominicana, Lasiodiploadia theobromae, and Phomopsis mangiferae [5]. Anthracnose affects the flowers, leaves and

fruit at various stages of growth and is a major cause of fruit rots in the postharvest supply chain [5]. It is now recognized as the most important field and post-harvest disease of mango worldwide. It is the major disease limiting fruit production in all countries where mangoes are grown, especially where high humidity prevails during the cropping season [10]. On mango, anthracnose symptoms occur on leaves, twigs, petioles, flower clusters (panicles), and fruits. On leaves, lesions start as small, angular, brown to black spots that can enlarge to form extensive dead areas. The lesions may drop out of leaves during dry weather. The first symptoms on panicles are small black or dark-brown spots, which can enlarge, coalesce, and kill the flowers before fruits are produced, greatly reducing yield. Petioles, twigs, and stems are also susceptible and develop the typical black, expanding lesions found on fruits, leaves and flowers [19]. Thus, the study will only focus in the fruits of mango to determine the severity of anthracnose disease.

Figure 2.1 Mango Anthracnose Disease in Fruits

Postharvest diseases of mangoes fruit reduce the quality and saleable life by premature decay and producing off-flavours [6]. Post-harvest processing of mango fruits comprises a series of unit operations, including cleaning, waxing, sorting, grading, packing, transport, and storage; while grading being considered the most important post-harvest step. Grading based on geometry and shapes are the two major parameters that consumers identify with the quality of mango fruit. Moreover, mango fruits with abnormalities in shape do not meet quality requirements for export. Even though Bangladesh produces a substantial mango crop, and overseas demand is steadily increasing, current post-harvest processing, which is still predominantly performed manually, cannot meet international quality requirements. Farmers continue to examine and sort/grade harvested mangos by visual appearance. Traditional visual inspection is labor intensive, expensive, and prone to human error, leading to variability in the final product. Thus, there is a critical need to be able to quickly, accurately, and efficiently evaluate agricultural products without the use of human labor [17]. Last 2017, a study about defect identification and maturity detection of mango fruits using image analysis was conducted in India. The objective of this work is to develop an automated tool, which can be capable of identifying defect and detect maturity of mango fruits based on shape, size and color features by digital image analysis [32]. The features were almost the same, thus this technology uses other electronic devices. The flow of image processing was almost the same too. It starts in low level (acquisition and preprocessing), intermediate (separation and segmentation), and the high-level processing (image quantification).

Below are some code snippets that perform similar functions as the application needs.

Figure 2.2 Code snippet for shadow removal in OpenCV Figure 2.2 shows a code snippet for removing shadows using OpenCV’s cv2.createBackgroundSubtractorMOG2() function, a Gaussian Mixture-based Background/Foreground Segmentation Algorithm that converts shadows into grey pixels [5]. With this, shadows that might be captured will be cleared to make sure that there are no other interfering factors that possibly be calculated in accumulating the percentage of black lesions.

Figure 2.3 Code snippet for separating image in OpenCV. Figure 2.3 shows the code snippet for separating objects in an image using OpenCV’s canny edge detection that converts the image into binary image for better accuracy when applying contour [10]. This will be applied to the sample that will be captured. The contour of the mango will be detected to make sure that the percentage to be evaluated only focuses on the surface of the mango.

Related System This part presented the related systems that support and serve as guide of the current study. Color Grab (Color Detection) Color Grab is the ultimate on-the-go color tool. It picks, captures, and recognizes colors simply by pointing the camera. It is a leading and worldwide application used by designers, artists, professionals, developers, and colorblinds. This said application, captures images and applies image processing for color recognition [34].

Figure 2.4 Color Grab application snapshot Figure 2.2 shows the snapshot of Color Grab application snapshot. This application is quite similar to DigiMango when it comes to image processing and color recognition. It processes images and has color detection function basically on the black lesion in a mango fruit. The difference is that DigiMango quantifies the percentage of the color in a certain phase.

PlantEX PlantEX is an application used to identify and detect the plant’s diseases from its leaves by image processing, deep learning, and machine learning techniques. The application can be used by capturing images of the plants leaf, PlantEx will then apply image processing techniques and also color segmentation and recognition, these techniques will be used to identify the disease the plant has. If the leaf is identified infected, the application will display possible treatment methods [35].

Figure 2.5 Plant X SnapShot Figure 2.3 shows the snapshot of PlantEX application. It captures images to identify what kind of plant it is from the selected or captured image. It is an image processing application, that uses color identification and segmentation same with DigiMango. The only difference is, this application is used and designed to identify the kind of plant can be seen from the selected or captured image while the DigIMango is designed to determine the severity percentage of the anthracnose disease of mango from the captured image.

PlantSnap – Identify Plants, Flowers, Trees & More PlantSnap application instantly identifies plant of all kinds, anywhere in the world. Flowers, trees, succulents, mushrooms and more can be quickly recognized with PlantSnap. It is built to help users identify flowers, plants, and tress in a snap. The application works by taking picture of the plant and then the image will be processed, in which color recognition and segmentation along with image recognition algorithm will be used to identify the image captured. The application holds almost 103,000 kinds of plant stored in a database and this datum will be the basis of the image comparison [33].

Figure 2.6 PlantSnap Figure 2.4 shows the snapshot of PlantSnap application. It is an Android mobile application used to identify plants, flowers, trees, and more using image processing. This is similar to DigIMango because it also uses image processing, color segmentation, color recognition, and captures image using a camera.

Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis This application uses image processing and computer vision systems to identify defect and maturity of mango fruits based on shape, size and color features by digital image analysis [23]. The application has similarities with DigiMango when it comes to their functions, the disadvantage of the application is that, it is not portable and that the system needs many of additional devices before it can be used effectively.

Figure 2.7 Mango Defect and Maturity Detector

Figure 2.7 shows the snapshot of Mango Defect and Maturity Detector. In the image the process of evaluating the defect and maturity of mango is on going. Here, the system needs those devices to be able to produce accurate results.

Snthesis

Table 2.1 Comparison to Related System Feature

Image Processing

Color Grab (color detection



Color Segmentation Color Recognition Image Capture from android camera

PlantEX

PlantSnap

Mango Defect and Maturity Detection

Mango Anthracnose Rating App





























Image quantification











Table 2.1 Illustrates the comparison of the Application and its related system discussed above. In achieving the related systems output, some of the few steps in attaining its results are closely related to the system that will be developed. This includes color recognition. Thus, the said system performs different set of task that serve their purpose.

Synthesis The proponents found out studies and systems that are similar to the proposed study and are relevant in the research and development of DigiMango. Among the related system discussed, Mango Defect and Maturity Detection is the closest application that has similar functionalities of DigIMango. However, at some point, the objective of both applications differs. Moreover, all the related studies mentioned above are very useful guide to the research and development of DigIMango, a mobile application that evaluates anthracnose disease severity on a mango fruit Technical Background The project’s android application utilized a smartphone camera for capturing and analyzing digital images of post-harvested mango samples. The application used Android Studio as the IDE of the application. OpenCV is used as the library in creating the application with Tensorflow as a library that trained the application not to recognize other objects aside from mango. In installing the application, a mobile android phone with Android OS version 6.0 (marshmallow) to the latest version is applicable.

Software Requirements The software listed below are necessary requirements needed for the development of DigIMango. Table 2.2 Software Specifications Software

Description

Fluid UI

This was used in creating the application’s design

Android Studio

The IDE that was used in developing the app.

Android OS

OS version 6.0 or higher.

Windows OS

Windows 7 or latest. This was used as the building platform of the app.

SQLite Database

Database used by the application.

Table 2.2 shows all the needed software requirements for the development of the project. Fluid UI was used in creating the application design; Android Studio was the IDE in developing the application; Android OS version 5.0 and above is recommended for the application to run. Lastly, Windows 7 and the higher version of Windows were used for the installation platform of all the software needed in the Applications development.

Hardware Requirements The hardware listed below are necessary requirements for the development of DigIMango. Table 2.3 Hardware Specifications Hardware

Recommended

Android Phone

Qualcomm 430 CPU, 2GB RAM, 4GB ROM

Back Camera

8MP and up

Table 2.3 shows all the needed hardware requirements to be used in the project. In using the Android application, Qualcomm 430 CPU, 2GB RAM, 4GB ROM with 8MP and up is recommended for faster and more efficient processing time. User Classification The user of this application are the plant technicians specially those who specialize in mango diseases. The researchers are among the probable users as well as farmers who are knowledgeable in the said disease.

Table 2.4 User Specification User Plant technicians

Recommendations Knowledgeable/ Expert/ Specialized

Researchers

Knowledgeable/Expert

Farmer

Knowledgeable/Expert

Table 2.4 illustrates the specification of the application user, which are the people who can use the application at their benefit. In using the Application, it is a must that the user has knowledge on certain diseases on mangoes. Additionally, for the Application to be of help to the user they must know how to navigate the smart phones. It is the device where the Application will be installed. This is for them to use the application properly. If they do not have the capacity to use the application, the proponents will guide and teach the user the appropriate things to do.

System Architecture System architecture illustrates how the application and the user interacts, additionally system architecture represents the structure and behavior of the system.

Figure 2.8 System Architure of DigiMango Figure 2.8 illustrates the interaction between the user and the system. As the user captures and send images to the Application of mangoes that are infected with the disease. The Application will then evaluate, analyze and process the image and will display the result to the user.

CHAPTER III METHODOLOGY System Development Methodology A system development methodology refers to the framework used to structure, plan, and control the process of developing an information system [24]. In achieving the goal of the system, there is a need to have a methodological approach in order to meet the time against physical and human constraints, as well as following the specific steps to attain most desirable result. The developers of DigIMango will use Mobile Application Development Lifecycle (MADLC) in the process of crafting the project. Following the framework of MADLC will help the developers build robust and optimal control application. The project will follow the seven phases of MADLC as shown below.

IDENTIFICATION

DESIGNING DEVELOPMENT PROTOTYPING TESTING DEPLOYMENT MAINTENANCE

Figure 3.1 Mobile Application Development Lifecycle

Figure 3.1 illustrates the methodology that will be used by the developers in the project. There are seven (7) phases in the methodology. It is expected to produce high quality functions and robust application that will meet the users’ needs at the given constraints. Mobile Application Development Lifecycle Process Identification Phase The first phase is the formation of the application. In this phase, the group visited different offices that might have had ideas and knowledge about a probable application that will meet the standards of the study that will be conducted. Through a plant pathology expert, the group have known about the post-harvest of mango and learned that the said studies concerning this disease needs technology based calculation in quantifying the amount of calculation in one fruit. The group conducted an interview and discussion that was also being followed by another with the same expert and office who explained how the system will work. With the knowledge shared by the expert, who is well versed about the mango fruit’s disease, its preventive measures, and its cure developers conceptualized the project and gathered more ideas thru series of interviews and after such things the developers planned and think through a project that has the capability to address the proponent’s problem when it comes to quantification of the said disease. The developers thought through the probability of the project and discussed the risk and the problems that might occur while it is in the

developing process. After the discussion the developers listed the problems that need to be addressed. 1. How is inaccuracy due to the shadow produced when acquiring the image be solved? 2. How is rating individual mangoes in bulk scanning done? 3. How is the percentage of lesions in the mango quantified? Use Case Diagram The use case diagram shows what the user is capable of doing in the system. This part shows the interaction of the user and the application.

Figure 3.2 Use case diagram of DigIMango. Use case diagram is a helpful tool to be able to present the system requirements and data flow. Figure 3.2 illustrates the processes that can be performed by the users while using the application. Additionally, these functions are the minimum requirements that the system has to perform to be able to meet user’s requirements.

Use Case Specification Table 3.1 Capture Photo (Mango Fruit) Use Case Description Use Case Name Scenario:

Capture photo (individual or bulk entries of mango) Lets the user pick between individual or bulk entries in capturing photo of mangoes.

Triggering Event:

After tapping the camera icon.

Brief Description:

After installing the application, the user can now captures photos.

Actors:

User

Related User Case:

None

Stakeholders:

Actor that captures photos of mango fruit.

Preconditions:

Application installed.

Post conditions:

The actor now views results.

Flow of Activities:

Actor

System

1. Actor install the application 1.1 System analyzes to an android device. the photo of mango 2. Actor captures images of fruit samples. mango fruit samples.

Table 3.2 View Results/Reports of Evaluation Use Case Description Use Case Name

View result/report of evaluation

Scenario:

The actor views result of mango anthracnose every after capture of the user.

Triggering Event:

After capturing and analyzing the photo.

Brief Description:

The actor views result after capturing and analyzing the mango.

Actors:

User

Related User Case:

None

Stakeholders:

Actors that will use the reports and results of the application.

Preconditions:

Mango fruits are captured.

Post conditions:

The actor now views results, and has to decide to “save” or “delete”.

Flow of Activities:

Actor

1. Captures images of mango fruit samples. 2. Views the result of the evaluation

System

1.1 System analyzes the photo of the mango fruit samples.

Table 3.3 Save or Delete Results Use Case Description Use Case Name

Save or delete results

Scenario:

After analyzing the mango fruit samples, the actor has the discretion whether to save or delete the results.

Triggering Event:

After analyzing the photo, and giving the results.

Brief Description:

The user can delete or save the result after analyzing it.

Actors:

User

Related User Case:

None

Stakeholders:

Actors that will use the reports and results of the application.

Preconditions:

Results are shown.

Post conditions:

The actors can view the results anytime after it has been saved.

Flow of Activities:

Actor 1. Click save or delete.

System 1.1 If “delete” is clicked, the results will not be available. Hence, if “save” is clicked, it will be in Archive and will be available for future use.

Table 3.4 Search Saved Results Use Case Description Use Case Name

Search saved results

Scenario:

The actor searches for past result/reports being saved

Triggering Event:

When archived search box is being inputted.

Brief Description:

The actor searches results/reports of samples that is in the archived part.

Actors:

User

Related User Case:

None

Stakeholders:

Actors that will use the reports and results of the application.

Preconditions:

Results are saved.

Post conditions:

The actors can view the results.

Flow of Activities:

Actor

1. Searches title search box.

System

in

the

1.1 Show the search results that has been saved.

Design Phase

Figure 3.3 Design Phase After gathering information and forming the idea into possible system, the second phase are all about designing the flow and conceptualizing the probable outcome and functions of the systems. The application was being checked by the group if it could be possible to be available in a multi-platform after a series of research and thorough study the group have found out that the application can only be available in one platform and it is in android. Android platform is selected and after those a series of design plan was being created such as the activity flow, database design and all the work flows that will help in the formation of the system. The following are further discussed below. Developing an initial design for the Application is also done in this phase. In which it mock ups was created and documented, noted that it

will be followed, modules were also which will hold the different functions of the system that will be the backend of architecture design. Through thorough planning and analysis as to flow, functions and operations that must be present in the system, the developers used diagrams and tables to explain and picture out the system that will be developed. Activity Diagram An activity diagram visually presents a series of actions or flow of control in a system. Activities modeled can be sequential and concurrent [8].

Figure 3. 4 Activity Diagram of DigIMango Figure 3.3 illustrates how the application will run the moment it is used and installed on a mobile device. Before allowing to capture fruit

samples, the application will let the user choose as to the kind of entry will be subject for analysis. Capturing the samples would then take place, after analyzing the samples, the Application will let the user decide whether to save or delete the results. Saving the results will allow them to view the results until the moment it is deleted. Functional Requirements Listed below are the functional requirements of the system. In the priority column, the following short hands are used: M – Mandatory requirements (something the system must do) D – Desirable requirements (something the system preferable should

do)

O – Optional requirements (something the system may do)

Table 3.5 Functional Requirements No

Requirement ID

Requirement’s Description

Priority RQ_01

CAPTURE PHOTOS (MANGO FRUIT INDIVIDUAL OR BULK ENTRY)

1

RQ_01_01

User captures photos (mango samples)

RQ_02

ANALYZE SAMPLES

RQ_02_01

System analyzes the percentage of black lesions M

RQ_03

VIEW RESULTS

3

RQ_02_01

User views the current analyzed samples

4

RQ_02_02

User views past saved sample results.

RQ_04

SAVE RESULTS

RQ_04_01

User saves result. Saved results will be

2

5

M

.

M M

M

located in archive module.

6

7

RQ_05

DELETE RESULTS

RQ_05_01

User deletes current result.

RQ_06

SARCH RESULTS

RQ_06_01

User searches for the saved results

M

D

______________________________________________________________

Table 3.6 explains the functional requirements of DigIMango. These requirements will be the bases of the Application’s performance testing. After the development of the system, it is expected to perform the following requirements, especially the requirements that are labeled mandatory. System Flowchart A flowchart is a representation of the step by step process and decisions needed to perform a process [28]. The system flowchart basically explains the workflow of the system.

Figure 3.5 System Flow Chart Figure 3.4 illustrates the data flow of the whole system, including the possible outcomes in making decisions in operating the system.

Entity Relationship Diagram (ERD) An entity relationship diagram (ERD) shows the relationships of entity sets stored in a database. An entity in this context is an object, a component of data. An entity set is a collection of similar entities. These entities can have attributes that define its properties [27].

Figure 3.6 Entity Relationship Diagram Figure 3.6 illustrates the data that will be stored in the database and as to how these data relate to each other. The table “research” will hold all the background information regarding the samples. This includes research name, source, tree size, treatment and replicates together with the total assessment of the transaction and the average area of infection. Lastly, is the “mango” table holds the results for individual samples and is linked to the “research” table through the foreign key “r_id”. These data will be the one to be used in delivering the information needed by the user of the application.

Data Flow Diagram A data flow diagram shows how the data are processed in the system specifically the input and outputs. Data flow diagram mainly focuses on the flow of information, on who give inputs, what outputs are produced and also where it is stored [27].

Figure 3.7 Data Flow Diagram Levels 0 The figure above illustrates the main function of the system which shows an overview of how the system would work.

Figure 3.8 Data Flow Diagram Levels 1 Figure 3.8 shows how the information is being processed. It elaborates the flow of function and data in the Application. Level 1 data flow diagram is a deeper illustration of the flow of the system.

Development Phase In this phase, the application that is being conceptualized and thoroughly designed is being coded. In the methodology used, coding for different modules of the same prototype can proceed in parallel. The development process can be in two stages: Coding for Functional Requirement and Coding for UI requirements. The code is developed first for the core functionalities. Parallel development can be done for modules of the same prototypes that are independent of each other. Subsequently, these modules can be integrated [32]. The flow and the functions illustrated and discussed below will serve as the map in developing the system.

Figure 3.9 Development Phase In this phase, the developers took in consideration the techniques that will be used to solve the problem on how to achieve the project’s objectives and eventually turn it into code that will comprise the project’s android application.

This work also includes using a flow chart to ensure that the process of the system is properly organized. First, images of the fruit(s) are acquired using high resolution camera to get better results and efficiency. Image processing techniques are then applied to these images to extract useful features which will be required for further analysis.

Image Aquisition

Image Pre-processing

Image Segmentation

Image Quantification

Image Separation

Figure 3.10 Block Diagram of proposed approach Figure 3.10 shows a basic block diagram of the proposed Application. These includes the functions the developers would create for the three main problems they have encountered. Step-by-step explanation of the system is as follows:

A. Image Acquisition Two images of a fruit or set of fruits with both its sides are acquired using a camera. The fruit(s) is/are placed on a white background, as much as possible without any light reflection for better result. The camera is held horizontally to the plane of the fruit. The photograph distance is neither too close nor too far; it is adjusted in such a way that the photograph shows only background and the fruit itself. For bulk fruit acquisition, an approximate distance of 1 inch between each fruit should be strictly observed.

B. Image Pre-processing The captured images undergo preprocessing. In this approach the captured images are processed to remove shadows that could affect the result. These shadows are removed using OpenCV’s MOG2 background subtraction.

Figure 3.11 Code snippet for shadow removal in OpenCV. Figure 3.11 shows a code snippet for removing shadows using OpenCV’s Gaussian

cv2.createBackgroundSubtractorMOG2() Mixture-based

Background/Foreground

Algorithm that converts shadows into grey pixels [12].

function,

a

Segmentation

C. Image Separation This step is only applied in bulk scanning of the mango fruits. This is necessary to examine each mango fruit individually. Each image is separated by finding contours in the image through OpenCV’s canny edge and contour function. Contour is a useful tool for shape analysis and object detection and recognition [10].

D. Image Segmentation After applying pre-processing and image separation (only applied in bulk scanning) stage, the outcome of image is fed to the segmentation stage. Segmentation is carried out based on histogram thresholding and morphological operations [30]. Here, color space segmentation is used by converting image into HSV color space. The image will be processed by segmenting the color between the mango and the background before collecting the value of the mango to ensure that the color of the background does not interfere in quantification of the image.

E. Image Quantification The image result from segmentation will then be used to quantify the anthracnose disease characterized by lesions in the fruit’s surface. Image quantification will start after collecting the values of the segmented image. Each color values will be assigned through HSV color space. Healthy surface will contain white pixels and surface with lesions will have black pixels. Calculating surface area and percentage of lesions are as done follows: 𝐴 = 𝑊𝑝𝑥 + 𝐵𝑝𝑥 𝐴 – 𝐵𝑝𝑥 𝑃=( ) × 100 𝐴 Where: 𝑊𝑝𝑥 = number of white pixels 𝐵𝑝𝑥 = number of black pixels 𝐴

= total surface area

𝑃

= percentage of surface lesions/anthracnose

With the use of this formulae gathered from Vithani [32], the detection and evaluation of black lesion on anthracnose in mangoes will be made possible.

Prototyping Phase

Figure 3.12 Prototyping Phase In this phase, the functional requirements of each prototype are analyzed. The prototypes are tested and sent to the client for feedback. After feedback is received from the client, the required changes are implemented through the development phase. When the second prototype is ready, it is integrated with the first prototype, tested and then sent to the client [32]. Failure of getting the clients’ approval of the prototype is subject for revision. The cycle will repeat until the application is finished and the expected results are met. The prototyping part aimed to make all functional requirements adhere to what is asked by the application. The developers did the design on paper first to leaving details behind. These mock-ups are made for the whole flow of the system. These mock-ups are subjected to checking and revision. Finally, the designs are coded and checked. This is where the developers made a prototype/model of the application that will be tested over and over until the desired result will be achieved.

After the application has completed the source code needed, the developers created a prototype/model that was utilized in the following period of the application's development. Mock-up

Figure 3.13 Application Mock-up

Testing Phase

Figure 3.14 Testing Phase Testing is one of the most important phases of any development lifecycle model. The testing of the prototype is performed on an emulator/simulator followed by testing on the real device [32]. After developing and prototyping the application, the next phase is the testing. Here, Application is evaluated to check if all the system requirements are made and the functions are executing well. Testing the application is carried out first by the developers. If bugs and other unwanted functions and errors will appear, changes will be made in the system. This is done in a cycle. After checking the functions of the system and passing the developer’s standards, this is submitted to the technical adviser for another assessment and evaluation. The technical adviser has the expertise in mango anthracnose severity rating. If system flaws occur, this will be returned to the developer for review. The application system will be ready for deployment after it passes the latter’s assessment and evaluation.

Deployment Phase

Figure 3.15 Deployment Phase Deployment is the final phase of the development process. After the testing is completed and the final feedback is obtained from the client, the Application is ready for the deployment [32]. This means that the product is ready to be used in the real environment by all end users of the product. The application will be uploaded to appropriate application store. This will be available to anyone who needs it. It will also be given to the proponents of the project who gave the developers the project idea. This project will be a significant tool that they could use in their line of work.

Maintenance Phase

Figure 3.16 Maintenance Phase The maintenance phase is the final phase and is a continuous process. Feedback is collected from users and required changes are made in the form of bug fixes or improvements. Appropriate security patches, performances improvements, additional functionality, new user interfaces should be provided at regular intervals in the form of updates to the Application. The maintenance phase also includes the marketing of the application: advertising and highlighting its unique features. If any application requires a backend server: this server and related operating system must be maintained as well [32]. Anything that needs to be done to improve the system, and to address some customer issues will be done in this phase. This will be a cycle until the application will be stable.

Chapter IV RESULTS AND DISCUSSIONS This chapter presented the result of the study. The following are the discussion of the outcome on each objective tackled in the previous chapters. Evaluate the percentage of anthracnose lesions in a mango fruit. In evaluating the percentage of anthracnose lesions in a mango fruit, it is required to either capture or gather from gallery four (4) sides of a mango as showed in figure 4.1 (a). These sides are the sides A and B that correspond to the front and back part of the mango and the sides C and D that correspond to the left and right part of the mango. After acquiring the image, the user can start the process by clicking the “start process” button as shown in figure 4.1 (b), the application evaluates and displays the percentage of anthracnose lesion found in four sides of mango fruit along with its level of infection.

Figure 4.1 (a) Image Acquisition Activity of the application. (b) Shows the evaluation result of the processed image.

Table 4.1 Accuracy test (evaluated by the expert) Test Rating of Expert (%)

Mobile Application Rating (%)

Remarks (0 or 1)

1

22.5

22.25

1

2

49

51.11

1

3

34

41.86

1

4

67.5

65.29

1

5

23

22.27

1

6

79

74.24

1

7

39

40.32

1 TOTAL:

7/7

REMARKS:

ACCEPTED

Legend: 0 = Rejected 1 = Accepted

PAIRED SAMPLE T-TEST RESULT Table 4.2 Paired Sample Statistics from accuracy test evaluated by the expert (Table 4.1). ______________________________________________________________

Pair 1

Mean

N

Std. Deviation

Application

45.3343

7

19.87121

Expert

44.8571

7

21.70007

Table 4.2 shows a paired test result of the application and the expert’s rating.

Mean is the average calculated from each rating method (through application and expert), N is the number of samples participated in the calculation, Std. Deviation is a measure that is used to quantify the amount of variation or dispersion of a set of rating values given. The mean rating of the application with 45.33 and the expert’s with 44.56 is almost the similar, same goes for its standard deviation. Therefore, the researchers conclude that the application’s rating is acceptable and statistically not-significantly different.

Table 4.3 Paired Sample Correlation from accuracy test evaluated by the expert (Table 4.1). ______________________________________________________________

Pair 1

Application & Expert

N

Correlation

7

.986

Sig. 0.000047

Table 4.3 shows the correlation of 0.986 (1 as the highest) with the significance of 0.000047 of the application and the expert’s rating. Therefore, the researchers were confident that the application’s rating is matched and is positively correlated with the expert’s rating.

Table 4.4 Paired Samples Test from accuracy test evaluated by the expert (Table 4.1). 95% Confidence Interval of the Difference

Pair 1

ApplicationExpert

Mean

Std. Deviation

Std. Error Mean

Lower

Upper

t

dt

Sig. (2tailed)

.47717

3.96900

1.50014

-3.14785

4.14785

.318

6

.761

Table 4.4 shows the t-value (t) of 0.318, degrees of freedom (df) of 6, a 95% Confidence Interval of the Difference with the lower-bound of -3.19367 and an upper-bound of 4.14785. The table also shows a probability of a given t-value or can be referred as p-value (Sig (2-tailed)) of 0.761. With the constant critical value (cv) of df-6 that is 2.447, the researchers observed that the t-value is less than the critical value. The p-value 0.761 is greater than 0.05 (a conventional (and arbitrary) threshold for declaring statistical significance is a p-value of less than 0.05). Also, the Confidence Interval of the difference crosses the zero value (a confidence interval around a difference that does not cross zero also indicates statistical significance). All these observations indicate that the samples that are tested (application’s and expert’s rating) are statistically not-significantly different. This means that the expert’s rating results is not different (statistically) from the application’s rating results.

Calculate the rating of anthracnose disease on mango fruit at the maximum of ten (10) samples in one transaction. Shown in figure 4.2 are sample photos of ten (10) mango fruit that will be calculated. The mango will be evaluated individually. In making the individual rating, the photos of a mango will be cropped and paired to its four sides, front side, back side, left side and right side. To get the precise results, there are things that need to be considered such as, the mangoes must be at least one (1) inch apart from each other, this is for the mango to be cropped accordingly. Since ten mangoes will not fit in one row with the allowable size of the background, the arrangement must be in two rows with the same distance from each other. Note that in the process of capturing the four sides of the mango, the mango must not be interchanged for it significantly affects the pairing and results of the rating. Shown in figure 4.3 is the result of calculating ten mangoes in one transaction and the average evaluation of anthracnose lesions on all mangoes.

Figure 4.2 Position of 10 mangoes when captured.

Figure 4.3 Individual result of 10 mangoes evaluation Store the past ratings of projects that has been analyzed. This functionality of the application shows the results of the transactions that are being saved by the user. Saving results can be done after the evaluation display and input of details. The analyzed results, are the information about the samples such as the research name, treatment used, number of replicate, source and tree size. Shown in the figures 4.4 (a) is the archive view of the application, and in 4.4 (b) is the transaction result view. This functionality is beneficial for the user for it stores information that which they can use in their line of work. With this, shorter time will be taken to administer the research, and easier in viewing result for it has saving options in which results can be viewed anytime.

(a)

(b)

Figure 4.4 Screen Shot of Archive View

TEST CASES Test Case 1 The test performed in DigiMango is the image acquisition and the evaluation of mango samples. The application evaluates and calculates the percentage of black lesion in the mango samples.

(a)

(b)

Figure 4.5 Evaluation of black lesion in mango fruits. The figures above show how the image is being acquired, in which the user must capture four (4) sides of the mango samples consequently as shown in (Figure 4.5 a). The process of evaluation of the mango happens when the user clicks the “start process” button. After doing so, the application will display the individual evaluation and calculation of the sample fruits

Test Case 2 The test case 2 shows the saving results module of DigiMango. The application will save result of the transactions done depending on the user’s choice.

(a)

(b)

(c)

Figure 4.6 Saving Transaction Results. Figure 4.6. show how transaction are being processed. After the image calculation, and the user wants to save the results of the test, the user must write the necessary details as shown in (Figure 4.6 a) to identify one result from the other. Aside from saving the application, a selection to add more mango is also available. Since the application is made to calculate only ten mangoes at a time, this functionality is added so that the user can store more than ten mangoes in one file. Shown in (Figure 4.6 b) is the archive view of the application, where all of the past saved results are shown. Lastly, shown in (Figure 4.6 c) is an example of the application result view.

Test Case 3 The test case three is the constrain module of DigiMango. The module that restricts the calculation of any other fruits or objects.

Figure 4.7 Restriction prompt Figure 4.7 shows an example of the application constraint, in which when other type of fruit is captured the application will not accept it, and will then ask the user to take other photo. Additionally, it is also advised that in capturing the mango, the capture must fit the background to its frame.

Table 4.5 Consistency trial on severity of mango anthracnose caused by Colletotrichum gloeosporioides using DigiMango

Fruit

Number of Trials

Total

Mean

No. 1

2

3

4

5

6

7

8

9

10

1

26.35

26.37

27.64

26.38

25.39

26.71

26.25

27.90

26.98

26.12

266.09

26.609

2

50.33

50.75

50.48

50.34

51.14

51.79

51.11

50.85

52.26

53.22

512.27

51.227

3

40.18

41.20

42.33

42.36

42.40

42.87

41.86

40.68

42.33

42.51

418.72

41.872

4

63.74

63.43

64.29

65.17

64.67

65.34

65.29

64.27

65.58

65.17

646.95

64.695

5

22.91

24.04

24.29

23.84

23.55

24.23

22.27

22.07

25.93

23.90

237.03

23.703

6

78.52

75.98

73.20

75.09

76.29

75.96

74.24

74.29

76.49

76.06

756.12

75.612

7

41.44

41.69

41.89

39.25

40.40

40.47

40.32

36.18

42.48

42.01

406.13

40.613

8

74.89

74.27

74.67

74.45

73.40

74.21

75.10

75.36

73.02

74.12

743.49

74.349

9

33.02

33.53

31.23

32.22

33.60

33.75

34.21

34.01

33.64

34.67

333.88

33.388

10

48.90

49.04

49.46

48.84

48.65

48.94

48.34

49.59

48.34

48.65

488.75

48.875

Table 4.5 shows the consistency trial done in the testing phase of the application.

Chapter V CONCLUSION AND RECOMMENDATIONS Conclusion The objectives of the study were all met by the developers during the phases allotted for the specific task these objectives are namely: (1) evaluate the percentage of anthracnose lesions in a mango fruit, in which in every transaction a rating of the evaluation of anthracnose on mangoes was presented individually. (2) Calculate the rating of anthracnose disease on mango fruit at the maximum of ten (10) samples in one transaction, where the user can assess ten (10) mangoes at the same time, additionally the users can add more mangoes by doing another transaction in one project this is by the feature “add more mangoes. Lastly, (3) store the past ratings of projects that have been analyzed, wherein the user can save the transaction together with the additional samples information. This application is available offline and can be accessed through android smartphones. In the process of crafting the application, the developers had gone through thorough experimentation to produce accurate results and by such, the developers found out few things that need to be considered while using the application. (1) Luminance. This factor has massive effect to the result of the application, therefore it is added to the scope and limitations that the application must be used indoor with a bright light coming from the fluorescent. (2) Turning on smartphone flash. When taking image against the light shadows might appear in the image and will affect the processing of the image that is why it is advised to turn on the flash. (3) Distance of the phone to the subject. In testing

the application, the developers found out that the smartphone cameras have different angles when it comes to focus, other cameras exhibit wide focus and others are narrow. With this, the developer added that in capturing the samples, the white cloth or the paper that will be used as background must be fit in the frame. (4) Capturing samples in four sides. Initially the application is planned to be captured in just two sides, but later on as the testing phase goes, the developers and their technical expert noticed that there are parts of the mangoes that cannot be calculated. This is because a mango is a three dimensional object and an image from the phone capture can just be evaluated in a two dimensional manner thus results vary. With this, the technical expert advice that the application should capture all sides of the mango and just do the manipulation in the code which is earnestly obeyed by the developers. Additionally, the developers found out that there are slight differences to the results when using different smartphone brand, and as we dig dip into the reasons of dissimilarities, we have found out that different smartphones exhibit different camera lens and sensor that give different image results even when the cameras have the same megapixels. Mobile Application Development Lifecycle proved its effectiveness in developing mobile application. Through the phases in this methodology, the proponents were able to finish the application. Through the use of digital image processing, rating the severity of anthracnose disease on mangoes is made possible. Prior to the deployment of the application it was being tested and was proven effective as it reaches the 95% accuracy goal, given that all the discussed considerations above are followed accordingly.

Recommendations Based on the findings of the study, the following are recommended by the developers for future improvements, mobility and environment of DigiMango. 1. The application shall be able to run on IOS and Windows platforms and must not be limited to android phones only. 2. It shall be able to evaluate and calculate more than ten (10) mangoes in one process to lessen the time it would take in administering the researches. 3. It is recommended to the future developers to enable cloud computing backup functions to avoid probable data loss. 4. Adding web application that would automatically generate reports through excel is also recommended by the developers for better access.

Implementation Plan This part of the documentation focused mainly on the implementation of the strategic plans for application accomplishment. Tables were created that shows the list of activities together with the processes which the developers dealt with for the application to acquire the objectives of the study. A. Project Implementation Checklist In order to keep track of the development of the application. By completing this checklist, the proponents are assured that the application has met all the necessary functions the application must acquire.

Table 5.1 Project Implementation Checklist of DigiMango Tasks

Yes

No

Assessment

Project Development √

Completed

√

Completed

√

Completed

√

Completed

Is the interface design user-friendly?

√

Completed

Is system requirements properly analyzed?

√

Completed

Does the system logic associate to the

√

Completed

√

Completed

Does the project meet the expected objectives and limitations? Are all the requirements of the system met? Is the quality of the system highly achieved? System Analysis and Design Is the coding of the system suitable to the given requirements?

requirements? Installation Was the application properly installed on the mobile phone? Is the application compatible with the given

Completed

software requirements? System Developers and Users Does the user have knowledge regarding

√

Completed

√

Completed

the application? Are the developers dedicated throughout the system development?

Table 5.1 presents the list of implementation checklist done in the project. It shows that the application has met its expected objectives and system requirements asked by the user. The developers created a user friendly application that would satisfy the user s needs, and assured that the application can be successfully installed in a smartphone device which will met the hardware and software specification declared in Table 2.2 and Table 2.3. Furthermore, the developers evaluated through the prerequisites of the application and has done all the necessary measures to avoid problems in the said phase. B. Implementation Contingency A contingency plan was prepared by the developers to help deal with the certain problems that might occur in the application. Connection Failure The application is made to run in an offline environment which is why the problem with internet connecting is ceased to occur. User Involvement The application interface is made user friendly that is why users manipulation of the application is easier. A part of the application found in the menu called “How to use” is also created by the developers in which it has instructions how the application operates, together with the things the user must consider while using the application.

C. Infrastructure Development To ensure that the infrastructure implementation in inclined with the study’s objectives and making sure that the application is working accordingly when it is deployed to the user. In connection to this the developers examined the infrastructure and deployment of the application. 

Testing – it is a technique that allows the developers to identify the bugs and errors in the application. Testing a very important part in the application development, through this the developers can be able to promptly act to the situations in hand that might affect the other functions of the application.



Development – In this part the developer must meet all the requirements of the system, the objectives, specification, and user expectations.



People – It refers to the user of the application.



Tools – this refers to the tool that is used for the application to function, for this application a smartphone with at least Android marshmallow mobile OS.

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http://erepository.uonbi.ac.ke/bitstream/handle/11295/101898/Nj uguna_Evaluation%20of%20Mango%20%28Mangifera%20Indic a%20L.%29%20Mineral%20Nutrition%20on%20Jelly%20Seed %20Disorder%2C%20Fruit%20Yield%20and%20Quality.pdf?se quence=1&isAllowed=y

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Parvez, GM. M. (2016). “ Pharmacological Activities of Mango (Mangifera Indica) : A Review”, Journal of Pharmacognosy and Phytochemistry

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5(3):

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Sahu, D. & Potdar, R. M. (2017). Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis. American Journal of Artificial Intelligence. Vol. 1, No. 1, 2017, pp. 5-14. doi: 10.11648/j.ajai.20170101.12.

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file:///C:/Users/User/Downloads/Documents/The-Philippines-inthe-Mango-Global-Value-Chain.pdf [23]

Stark, K. F. & Couto, V. & Gereffi, G. (2017). The Philippines in the

Mango

Global

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APPENDIX A RELEVANT SOURCE CODE

APPENDIX B SAMPLE INPUT/OUTPUT INPUT

Output

APPENDIX C USERS MANUAL MAIN MENU

1

2

3

4

1. Capture/Select Photo Button – the button that sends the user to the main function of the application. 2. Archive Button – holds the past results that was being saved by the user. 3. How to Use Button – has the instruction on how to use the application. 4. Close App Button – closes the application

CAPTURING SAMPLES

1 3

2

4

1. Capture subject using phone cameras. 2. Choose subject image from stored images on gallery. 3. Copy Image from side A to B or from C to D. 4. Copy Image from side B to A or from D to C. 5. After all of the images are acquired the user can now click start process to view evaluation results. 6. Back to Main Menu.

5 6

INPUT SAMPLE INFORMATION/ SAVING RESULTS

3

1

4 5 6

2

1. Fill all text fields allotted for the subject’s information. 2. Click to show or hide other menu buttons. 3. Click to save results. 4. Every transaction can only cater 10 samples, if the samples are more than the number you can add another transaction. This button will allow you to save more than ten result. 5. Perform new test. 6. Back to Main Menu

2

ARCHIVE VIEW

1

2

4

3

1. 2. 3. 4.

Search saved results. Display in scrolled list saved result. Click to view other menus. Result Display.

3

5 7 8 6

5. Perform new test. 6. Back to main menu. 7. Delete the recorded result. 8. Search saved result.

6

APPENDIX D

CERTIFICATION This is to certify that the undersigned has reviewed and went through all the pages of the proposed project study / research entitled “DigiMango – A Digital Image Processing on Anthcnose Severity of Mango” developed by LOUIE G. SIMBAJON, GIA LORRAINE C. MATA and DESSA GRACE L. YBAÑEZ as against the set of structural rules that govern the composition of sentences, phrases, and words in the English language.

Signed:

Date Signed:

MS. DONNA G. MAGALLANES

December 5, 2018

Grammarian

APPENDIX E CURRICULUM VITAE

GIA LORRAINE C. MATA Prk. I-Rizal Brgy. Canocotan Tagum City 09098119069 [email protected]

OBJECTIVES: Seeking a challenging career that has progressive organization that provides an opportunity that will utilize my technical skills and abilities

EMPLOYMENT HISTORY Ideahub Solutions Inc. On-the-Job Trainee 3F/4F AMAARA Center Building 1045 Jacinto Extension Poblacion District Davao City July 2017- August 2017

KEY SKILLS: • • •

Competent in customer support skills of communication, attentiveness, respect, problem-solving abilities and organize. Capability of maintaining confidentiality of the office data, records, files and activities. Can perform task with less supervision.

EDUCATIONAL ATTAINMENT: Bachelor in Science Information Technology (BSIT) University of Southeastern Philippines Apokon, Tagum City 2015 - Present Tagum National Trade School Apokon, Tagum City 2011-2015

TECHNICAL COMPETENCE: • • •

PHP, Java Computer programming, Android. Photoshop, Video Editing Proficient in working on MS office applications of MS Word, MS Excel, and MS PowerPoint.

SEMINARS ATTENDED: Mental Health Care Awareness University of Southeastern Philippines (USeP) FTC Hall, Apokon, Tagum City March 20, 2018 Personality Development Seminar University of Southeastern Philippines (USeP) FTC Hall, Apokon, Tagum City

PERSONAL DATA: Birthday

:

September 16, 1998

Sex

:

Female

Civil Status :

Single

Height

:

5’2”

Weight

:

52 kg.

REFERENCE:

Ms. Fraulein B. Silva University of Southeastern Philippines Guidance In-charge 09460575259 Mr. Francisco C. Remitar Sangguniang Panlalawigan Board Member 09569845457

I declare that the above-written particulars are true and correct to the best of my knowledge and beliefs.

GIA LORRAINE C. MATA Applicant

LOUIE G. SIMBAJON Purok-10, Magdum, Tagum City, Davao Del Norte, 8100 +63 909-0914-607 [email protected]

OBJECTIVES: Seeking a challenging career that has progressive organization that provides an opportunity that will utilize my technical skills and abilities

EMPLOYMENT HISTORY New Options Worldwide (NOW) Outsourcing Services On-the-Job Trainee Pink Walters Building, 4th Floor Davao Del Sur PH, Quimpo Blvd, Talomo, Davao City, 8000 July 2017- August 2017

KEY SKILLS: • • •

Competent in customer support skills of communication, attentiveness, respect, problem-solving abilities and organize. Capability of maintaining confidentiality of the office data, records, files and activities. Can perform task with less supervision.

EDUCATIONAL ATTAINMENT:

Bachelor in Science Information Technology (BSIT) University of Southeastern Philippines Apokon, Tagum City 2015 - 2019 La Filipina National High School Apokon, Tagum City 2010-2014

TECHNICAL COMPETENCE: Languages: Web: Software:

Methods:

Java, C/C+/C#, Visual Basic, SQL, PHP, Android HTML, CSS, Javascript, PHP MS Visual Studio, MySQL, E-DrawMax, MS Project, MS Office, Adobe Photoshop, Adobe Audition, JCreator, Sony Vegas Pro, Sketch Up, Android Studio Object-Oriented Analysis/Design, Unified Modeling Language (UML), Extensive Mark-up Language (XML)

SEMINARS ATTENDED: Mental Health Care Awareness University of Southeastern Philippines (USeP) FTC Hall, Apokon, Tagum City March 20, 2018 Personality Development Seminar University of Southeastern Philippines (USeP) FTC Hall, Apokon, Tagum City Waste Management Seminar University of Southeastern Philippines (USeP) PECC Gym, Apokon, Tagum City Action Planning and Seminar Workshop University of Southeastern Philippines (USeP) Obrero, Davao City SSG Midyear Convention University of Southeastern Philippines (USeP) FTC Hall, Apokon, Tagum City Legislative Convention

University of Southeastern Philippines (USeP) Obrero, Davao City University Leadership Enhancement Training University of Southeastern Philippines (USeP) Bislig City

PERSONAL DATA: Birthday

:

September 29, 1998

Sex

:

Female

Civil Status :

Single

Height

:

6”

Weight

:

72 kg.

REFERENCE:

Ms. Fraulein B. Silva University of Southeastern Philippines Guidance In-charge 09460575259

I declare that the above-written particulars are true and correct to the best of my knowledge and beliefs.

LOUIE G. SIMBAJON Applicant