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Philippine Carabao Mango Pest Identification Using Convolutional Neural Network

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Philippine Carabao Mango Pest Identification Using Convolutional Neural Network INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020 ISSN 2277-8616 Philippine Carabao Mango Pest Identification Using Convolutional Neural Network Antonio V. Rocha IV, Joe G. Lagarteja Abstract— The detection and management control of pests in mangoes if applied properly would result to a higher fruit production. Applying precision agriculture with the use of modern technology helps mango farmers detect and identify the different types of pests that are infesting their farms. This research study introduces a computer application development that can identify and determine mango pests based on the provided images using a smartphone. Convolutional Neural Network (CNN) is a type of algorithm that was used to train stack of mango pest images which were pre-processed and used as a train model. Using Convolutional Neural Network (CNN), the images were processed to determine the type of pests currently present on mango trees and provides the best control measure that the system would provide after detection. The neural network was designed using Keras to run on top of the deep learning framework TensorFlow. Datasets composed of 4,300 images were used for training and 800 were used to validate the CNN model. The model achieved a remarkable 88.75% accuracy rate in determining mango pests. GIS (Geographical Information Systems) was also utilized to determine affected areas in the region. Index Terms— Artificial Intelligence, Carabao Mango Pest, Convolutional Neural Network, Geographical Information System, Pest Management and Control —————————— —————————— 1 INTRODUCTION distorted with ―shotholes‖ in various shapes and sizes. It also Mango fruit is one of the premium exports of the Philippines, blackens and withers the flowers and produces ―blossom and Region II is one of the major contributors of mango blight‖ while causing brown to black sunken spots on the fruits. production in our country. In fact, based on the report of the With the different types of pests and their effects in mangoes Philippine Mango Industry Foundation Inc. (PMIFI), the island already known for their characteristics, precision agriculture of Luzon produces 2/3 of the mangoes, and the Province of [2][3] is applied to assist mango growers in decision making Isabela is currently at Rank #9 that produces 18,185.99 metric about pest monitoring and control by utilizing Geographic tons of mangoes. But amid the recognition and increasing Information System (GIS) [4][5][6] for mapping of infected demand for mangoes, the country’s mango production areas of the mango farm, clustering algorithm, and using time declined drastically due to pest infestation which results in series analysis. Thus, the researchers developed the poor quality of fruits [1]. There are eight (8) different types of Philippine Mango Integrated Pest Management using pests that are common in mangoes in the Philippines which Convolutional Neural Network[7][8] that can easily detect pests includes Capsid Bug, Fruit Fly, Mango Cecid Fly, Mango that are currently present on mango trees through image Leafhoppers, Mango Seed Borer, Mealy Bugs, Scale Insect, capturing using a digital camera and then uploaded to the and Anthracnose. Capsid Bug stays in wild vegetation and is computer for processing which then provided the identification active in the evening. It feeds on developing fruits, up to the and treatment procedure to eliminate the said pest. The size of a chicken egg. Fruit Fly is almost similar in size to the Convolutional Neural Network (CNN) approach is used for the house fly. The female fruit fly punctures the peel of mature fruit classification and recognition [19] of the different pests, and for and lays eggs on it. Mango Cecid Fly is a very small and mango fruit defect identification by applying a classification delicate fly with long legs and antennae, and hairy transparent algorithm [9][10]. The Geographic Information System (GIS) wings. Its larvae are tiny bright yellow maggots. Mango was utilized to map/locate the infected areas of the mango Leafhopper pierce and suck plant sap resulting in the withering farm based from the area of interest. Time series analysis and falling of individual flowers. Mango Seed Borer larvae model was also applied in order to give farmers a concrete feed on the seed and flesh. A single larva can consume the decision-making on how to prevent/control pest infestation in entire seed in a short period of time. Mealy Bugs are small their farms [12]. (2mm long), oval-shaped, soft-bodied insects with white cottony filaments on their body. Mealy bugs suck vital plant 2 MATERIALS AND METHODS sap and affect leaves, flowers, and fruits. 2.1 Research Design Affected parts turn yellow, dry-up and eventually, fall-off. Scale The researchers used the descriptive and developmental Insects are usually circular in form with scale-like appearance. research method or approach. It is the most appropriate In the nursery, leaves of grafted mangoes are readily infested method of inquiry about the present status and condition of a with scale insects, causing them to dry and fall. On bearing particular phenomenon. Concepts and procedures of general trees, high population of scale insects causes blackening of description, analysis, and classifications are discussed and the canopy due to the growth of fungus ―sooty mold‖ which illustrated in a considerable detail. This method tends to both develops from their excreta (honey dew). Affected leaves are the qualitative and quantitative analysis of inquiry such as the covered with a thin, black, papery film, which produces present investigation. In constructing the project, the Rapid unsightly appearance. Anthracnose is a major post-harvest Application Development (RAD) was used [13][14]. problem of mango fruits and is the most serious fungal disease of mangoes in the Philippines. Anthracnose causes irregular brown spots on young leaves while mature leaves get ———————————————— Antonio V. Rocha IV is from Isabela State University, Jones Campus Joe G. Lagarteja is from Isabela State University, Echague Isabela 3443 IJSTR©2020 www.ijstr.org INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020 ISSN 2277-8616 2.2 Network Architecture Fig. 1. Rapid Application Development Cycle Fig. 2. Network Architecture The challenge facing software development organizations can be summarized as better and faster. The RAD development TABLE 1 path attacks these challenges head-on by providing a means HARDWARE REQUIREMENTS for developing systems faster, while reducing cost and Hardware Specification increasing quality. This life cycle has the following four stages, including the entire task and activities required for the Processor 7th Gen Intel® Core™ i7-8750H Processor implementation, development and design for the application (9M Cache, 2.2 GHz, 4.1 GHz max) system that could support the said requirements. RAM The following are the phases of SDLC: Memory Installed Size: 16 GB Technology: DDR4 Memory Speed: 2400 MHz Phase 1: Requirements Planning . Preliminary Investigation Hard drive Hard Disk: ITB Conduct interview for the following: Interface Type: M.2 SSD/PCIe NVMe o Mango Growers Digital Camera Smartphone with a minimum of 18- . Consolidation of the Gathered Information megapixel camera resolution . Conduct literature studies to find a similar case of existing research . Problem Identification . Conceptualization of possible solution to the TABLE 2 problem SOFTWARE REQUIREMENTS Software Specification Phase II: User Design The following minimum hardware and software requirement, The minimum requirement in terms of Operating System and system architecture were used in developing the Operating System is at least Windows 8/8.1 proposed Philippine Mango Integrated Pest Management uses Professional Edition or higher. Convolutional Neural Network. XAMPP Containing PHP and MySQL Browser Phase III. Rapid Construction Chrome, Mozilla Firefox, Microsoft Edge In this phase, the researchers focused on the rapid Python construction and application of the system. Software testing of Includes TensorFlow and Keras libraries the developed system is also performed. The study used Python programming language as its programming platform. Figure 2 shows how the developed system works. There are Phase IV. Implementation three (3) stages of procedures to be performed to be able to To evaluate the acceptability of the developed system, the achieve the intended results. The first stage is the Image researchers used ISO 25010 Standard. Acquisition which means the acquiring of images from the Pilot Test the system to IT Experts and Mango Growers. source and that comprises the manual capturing of image Capacitate users (Mango Growers) of the system through photo using a smartphone with a high-resolution camera from orientation and training. a mango tree which is suspected of having pest, which is then sent via the internet. The second stage is the Image Processing & Recognition that deals with the processing of the captured image which includes image filtering and thresholding. The image is partitioned into its foreground and background and isolates objects by converting it into a grayscale image, then to a binary image which will then be queried in the database. The detected pest is extracted from a 3444 IJSTR©2020 www.ijstr.org INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020 ISSN 2277-8616 set of image references, compare it on the training image to 2.5 Data Analysis
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