Developing deep learning architecture for image classification using convolutional neural network (CNN) algorithm in forest and field images

(1) Meiga Isyatan Mardiyah Mail (Universitas Islam Indonesia, Indonesia)
(2) * Tuti Purwaningsih Mail (Universitas Islam Indonesia, Indonesia)
*corresponding author

Abstract


Indonesia is an agricultural country with a variety of natural resources such as agriculture and plantations. Agriculture and plantations in Indonesia are diverse, such as rice fields that can produce rice, soybeans, corn, tubers, and others. Meanwhile, plantations in Indonesia are like forests with timber products, bamboo, eucalyptus oil, rattan, and others. However, rice fields, which are examples of agriculture, and forests that are examples of plantations, have the same characteristics. It is not easy to distinguish when viewed using aerial photographs or photographs taken from a certain height. For recognizing with certainty the shape of rice fields and forests when viewed using aerial photographs, it is necessary to establish a model that can accurately recognize the shape of rice fields and forest forms. A model is to utilize computational science to take information from digital images to recognize objects automatically. One method of deep learning that is currently developing is a Convolutional Neural Network (CNN). The CNN method enters (input data) in the form of an image or image. This method has a particular layer called the convulsive layer wherein an input image layer (input image) will produce a pattern of several parts of the image, which will be easier to classify later. The convolution layer has the function of learning images to be more efficient to be implemented. Therefore, researchers want to utilize this CNN method to classify forests and rice fields to distinguish the characteristics of forests and rice fields. Based on the classification results obtained by testing the accuracy of 90%. It can be concluded that the CNN method can classify images of forests and rice fields correctly.

Keywords


Convolutional Neural Network (CNN); Field Imagery; Forest Imagery; Classification

   

DOI

https://doi.org/10.31763/sitech.v1i2.160
      

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