Convolutional Neural Networks Implementation for Chili Classification

Purwaningsih, Tuti; Anjani, Imania Ayu; Utami, Pertiwi Bekti

doi:10.1109/sain.2018.8673373

Cited by 18 publications

(12 citation statements)

References 2 publications

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“…This study aims to determine the CNN method's facial images classification. In this study, the pretrained model used is the VGG-face model [8]. This significant result was obtained using 16-19 layer weights.…”

Section: Introductionmentioning

confidence: 98%

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Face Images Classification using VGG-CNN

Astawa¹,

Radhitya²,

Ardana³

et al. 2021

Kno. Eng. Da. Sc.

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Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image. The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%).

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Section: Introductionmentioning

confidence: 98%

“…Currently, studies that apply the deep learning method provide better results in facial recognition [7]. The most compelling image recognition method is Convolutional Neural Network (CNN) [8]. Recent researches results show that transfer learning solutions are the basis for image classification [7][9] [10].…”

Section: Introductionmentioning

confidence: 99%

Face Images Classification using VGG-CNN

Astawa¹,

Radhitya²,

Ardana³

et al. 2021

Kno. Eng. Da. Sc.

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“…The findings in this paper will create more opportunities for developing more accurate classifiers in the future. This is because the existing studies have only shown less than 90% accuracy on a particular type of chilli disease [18], [19]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Efficacy of chili plant diseases classification using deep learning: a preliminary study

Rozlan

Hanafi

2022

IJEECS

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<span>Plant disease classification using deep learning techniques is a popular research area due to the numerous opportunities for introducing advance and robust classifiers. Nevertheless, classifying chilli plant diseases accurately from images under uncontrolled environment and various imaging conditions remains unsolved due to the lack of chilli disease image datasets. In this study, the efficacy of three high-performance deep learning algorithms, namely VGG16, InceptionV3, and EfficientNetB0, in classifying three types of chilli leaves diseases, namely upward curling, mosaic/mottling, and the bacterial spot, is demonstrated. These methods are popularly used for other plant disease classifications due to their effectiveness. The experiments were performed on the 3,000 chilli plant disease images collected from three different field environments in Selangor, Malaysia. The images were captured with a complex background and various illuminations, angles, and distances to reflect the real-life scenarios. The complexity of the collected images was created based on the taxonomic information of chilli leaves diseases and the unavailability of chilli disease images under various imaging conditions in the publicly available plant disease databases. Experimented using appropriate specifications, the models demonstrated outstanding performance with more than 95% accuracy with the highest accuracy of 98.83% by InceptionV3.</span>

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“…CNN is the advancement from MLP (multi-layer perceptron) as one of the Deep Learning Algorithm. The CNN application has the most significant results in computer vision, this is often since CNN tries to imitate the image recognition framework within the human visual cortex, so that it has the capacity to handle image data [4].…”

Section: Introductionmentioning

confidence: 99%

convolutional neural network bird’s classification using north American bird images

Sitepu¹,

Liu²,

Sigiro³

et al. 2022

ijhs

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In general, image classification is more like to classify objects with large categories, where these objects have a low level of similarity that is relatively rare. Birds image classification is a tough image dataset annotated with many bird species. It may be a challenging issue as numerous of the species of birds have degree of visual closeness. Bird species recognition can be challenging for people, let alone computer vision calculations. To analyze the image, this paper resizes the image into 224224 pixels. We use the Convolutional Neural network (CNN) approach and add the structure of MobileNetV2, EfficientNetB0, EfficientNetB3 and the weight of the network that has previously trained using ImageNet. This paper attempts to analyze the comparative results from using MobileNetV2, EfficientNetB0 and EfficientNetB3 architecture. The F1 weighted average score MobileNetV2 is 75%, EfficientNet B0 is 81% and EfficientNet B3 is 81%. This result shows that some models are completely unable to predict some classes of birds, but other models can recognize them. This can happen because of differences of the operation in each CNN architecture parameter. These models complement each other.

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Convolutional Neural Networks Implementation for Chili Classification

Cited by 18 publications

References 2 publications

Face Images Classification using VGG-CNN

Face Images Classification using VGG-CNN

Efficacy of chili plant diseases classification using deep learning: a preliminary study

convolutional neural network bird’s classification using north American bird images

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