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

Mardiyah, Meiga Isyatan; Purwaningsih, Tuti

doi:10.31763/sitech.v1i2.160

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…YOLO is a state of art object detector which can perform object detection in real-time with good accuracy, YOLOv5 is a new release of the YOLO family of models that appeared on June 25, 2020. Used as one of the fastest algorithm that uses CNN for object detection combined bounding box prediction and object classification into a single end-to-end differentiable network, it can classify the image into a category, and can detect multiple objects within an image [17]. This algorithm applies a single neural network to the full image.…”

Section: Object Detection Methodsmentioning

confidence: 99%

Deep learning-based decision support system for weeds detection in wheat fields

Jabir

Falih

2022

IJECE

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<span>In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected.</span>

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Section: Object Detection Methodsmentioning

confidence: 99%

Deep learning-based decision support system for weeds detection in wheat fields

Jabir

Falih

2022

IJECE

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“…The primary image processing used is image acquisition, image preprocessing, and image segmentation [25]. The development used feature extraction and classification [26], [27] using the backpropagation neural networks method. This research process in detail used the following stages in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing

Saifullah

Suryotomo

2021

RESTI

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This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results.

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“…In 1989, Yann LeCun et al succeeded in classifying zip code images using a special case of Feed Forward Neural Network called Convolution Neural Network (CNN) [7]. Due to hardware limitations, Deep Learning was not developed further until 2009 where Jurgen developed a new method known as Recurrent Neural Network (RNN) which obtained significant results in handwriting recognition [11]. With the development of Graphical Processing Unit (GPU) hardware computing, the development of CNN models has become very rapid, in 2012, CNN methods can perform image recognition with accuracy that rivals humans on certain datasets [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Architecture Based on Convolutional Neural Network (Cnn) in Image Classification

Fawaidul Badri,

M. Taqijuddin Alawiy,

Eko Mulyanto Yuniarno

2023

kursor

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In current technological developments, Deep Learning is one of the most popular studies today, especially in the fields of machine learning and computer vision, GPU Acceration Technology is one of the reasons for the development of Deep Learning. Deep Learning has a very good ability to solve classic problems in the field of computer vision, one of which is in the case of object classification in images. one of the deep learning methods that is often used in image processing is the Convolution Neural Network (CNN) which is a development of the Multi Layer Perceptron method. This study uses the CNN architecture which consists of a convolution layer, as well as a fully connected layer, and will also determine the appropriate Optimizer and Loss function for CNN. The implementation of this method uses Google Colab (Tensorflow and Keras) with the Python programming language. In the training process using CNN, setting the number of epochs is done to improve accuracy in image classification, in the first scenario using epoch 20 produces an average accuracy of 99.45 with a loss value of 1.66. In the second scenario using epoch 15 produces an average accuracy value of 99.00 with a loss value of 2.92. then in the third scenario with a number of epochs 10 it produces an average accuracy value of 95.55 with a loss value of 95.55, while in the last scenario with a number of epochs 5 it produces an average accuracy value of 73.6 with a loss value of 51.92. From the 4 trial scenarios using the CNN method gives effective results and produces a fairly good accuracy value with an average accuracy and loss value of 99.99%. As well as the results of an average loss of 4.

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

Cited by 6 publications

References 17 publications

Deep learning-based decision support system for weeds detection in wheat fields

Deep learning-based decision support system for weeds detection in wheat fields

Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing

Deep Learning Architecture Based on Convolutional Neural Network (Cnn) in Image Classification

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