Hyperspectral Image Classification using Convolutional Neural Networks

Shambulinga, M; Sadashivappa, G.

doi:10.14569/ijacsa.2021.0120682

Cited by 3 publications

(2 citation statements)

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“…Wang et al presented a mushroom species classification system using a CNN. The authors used a dataset of 4,700 mushroom images belonging to 10 species and achieved an accuracy of 95.51% [5]. Wang et al proposed a mushroom classification system using a CNN.…”

Section: Literature Surveymentioning

confidence: 99%

Mushroom Classification

2023

IRJMETS

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Mushrooms are a popular food source around the world, but the risk of consuming poisonous mushrooms is a serious concern. In this project, we aim to classify mushrooms as either edible or poisonous using a convolutional neural network (CNN) in Python's Tkinter framework. Our dataset consists of images of various mushroom species, labelled as either edible or poisonous. We trained the CNN using the training dataset and tested its accuracy using the testing dataset. We also implemented a user-friendly graphical interface using Tkinter, allowing users to upload an image of a mushroom and receive an instant classification. Our results show that the CNN was able to accurately classify mushrooms with an average accuracy of 95%. The user interface proved to be effective in identifying mushrooms and providing clear results, making it a valuable tool for both mushroom enthusiasts and foragers alike. This project demonstrates the potential of using machine learning techniques to classify mushrooms and highlights the importance of accurate identification in preventing potentially deadly mushroom poisonings.

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Section: Literature Surveymentioning

confidence: 99%

Mushroom Classification

2023

IRJMETS

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“…Winiarti et al [10] conducted pre-training using CNN and classification with SVM in the tanning leather image case. Likewise, Shambulinga [11] performing feature extraction with CNN and classification using a support vector machine.…”

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confidence: 99%

Classification of clove types using convolution neural network algorithm with optimizing hyperparamters

Tempola,

Wardoyo,

Musdholifah

et al. 2024

Bulletin EEI

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This study uses clove imagery by classifying it according to ISO 2254-2004 standards: whole, headless, and mother clove. This type of clove will affect the quality and economic value when it has been dried. For this reason, it is necessary to take a first step to control cloves' quality. One way is to classify it from the start. This research will utilize the convolution neural network algorithm and compare it with model transfer learning and modified VGG16 architecture on clove images. In addition, research is also looking for the most optimal hyperparameter. The results of this study indicate that the application of convolution neural network (CNN) to clove images obtains an accuracy value of 84% using a hyperparameter of 50 epochs, a learning rate of 0.001, and a batch size of 16. Meanwhile, for the application of transfer learning VGG16, Resnet50, MobileNetV2, InceptionV3, DensetNet151, and modified VGG16 have respectively each of the highest accuracy including 95.70%, 76.15%, 96.89%, 98.07%, 98.96%, and 99.11%.

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