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Title: :  A REVIEW PAPER ON CNN APPROACHES FOR POISONOUS MUSHROOM IDENTIFICATION
PaperId: :  25516
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 6 2024
DUI:    16.0415/IJARIIE-25516
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Kelvin Dmello Alva’s Institute of Engineering and Technology, Karnataka
B S Sumukha Alva’s Institute of Engineering and Technology, Karnataka
Akash Devadiga Alva’s Institute of Engineering and Technology, Karnataka
SoorajAlva’s Institute of Engineering and Technology, Karnataka
Charan S VAlva’s Institute of Engineering and Technology, Karnataka

Abstract

Information Science and Engineering
CNN, ResNet-50, Mushroom Classification, Toxic Mushroom Detection, Image Processing, TensorFlow, Keras, NumPy, Pandas, Matplotlib, Scikit-learn, and Pillow.
Mushrooms are popular due to their high nutritional value, which includes amino acids, polysaccharides, and other essential nutrients. However, eating poisonous mushrooms can cause serious health problems like nausea, vomiting, mental disorders, acute anemia, and even death. Every year, approximately 8,000 people suffer from mushroom poisoning, with 70 fatalities. With thousands of mushroom species, only about 900 are edible, making it difficult for those without specialized knowledge to distinguish between them. While many people believe that poisonous mushrooms are brightly colored, some dangerous species do not exhibit this characteristic. allowing for further advancements in mushroom toxicity detection and classification. To reduce the risks associated with eating toxic mushrooms, this study proposes using deep learning techniques to classify mushrooms as edible or poisonous based on image features such as color, shape, and texture. A dataset containing 5,127 edible and 3,163 poisonous mushroom images will be used for training, validation, and testing, with a 70% training, 15% validation, and 15% testing ratio. The ResNet50 algorithm, a deep learning architecture known for its performance in image classification, will be used. TensorFlow and Keras will be used to implement deep learning, NumPy and Pandas to manipulate data, Matplotlib to visualize, Scikit-learn to evaluate models, and Pillow to preprocess images. The model aims to provide accurate predictions while taking into account the benefits and side effects of various mushrooms. This solution, by automating the identification of toxic and edible mushrooms, will help reduce the risks associated with foraging. while also contributing to public safety.

Citations

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IJARIIE Kelvin Dmello , B S Sumukha , Akash Devadiga , Sooraj, and Charan S V. "A REVIEW PAPER ON CNN APPROACHES FOR POISONOUS MUSHROOM IDENTIFICATION" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 6 2024 Page 1807-1812
MLA Kelvin Dmello , B S Sumukha , Akash Devadiga , Sooraj, and Charan S V. "A REVIEW PAPER ON CNN APPROACHES FOR POISONOUS MUSHROOM IDENTIFICATION." International Journal Of Advance Research And Innovative Ideas In Education 10.6(2024) : 1807-1812.
APA Kelvin Dmello , B S Sumukha , Akash Devadiga , Sooraj, & Charan S V. (2024). A REVIEW PAPER ON CNN APPROACHES FOR POISONOUS MUSHROOM IDENTIFICATION. International Journal Of Advance Research And Innovative Ideas In Education, 10(6), 1807-1812.
Chicago Kelvin Dmello , B S Sumukha , Akash Devadiga , Sooraj, and Charan S V. "A REVIEW PAPER ON CNN APPROACHES FOR POISONOUS MUSHROOM IDENTIFICATION." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 6 (2024) : 1807-1812.
Oxford Kelvin Dmello , B S Sumukha , Akash Devadiga , Sooraj, and Charan S V. 'A REVIEW PAPER ON CNN APPROACHES FOR POISONOUS MUSHROOM IDENTIFICATION', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 6, 2024, p. 1807-1812. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/A_REVIEW_PAPER_ON_CNN_APPROACHES_FOR_POISONOUS_MUSHROOM_IDENTIFICATION_ijariie25516.pdf (Accessed : ).
Harvard Kelvin Dmello , B S Sumukha , Akash Devadiga , Sooraj, and Charan S V. (2024) 'A REVIEW PAPER ON CNN APPROACHES FOR POISONOUS MUSHROOM IDENTIFICATION', International Journal Of Advance Research And Innovative Ideas In Education, 10(6), pp. 1807-1812IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/A_REVIEW_PAPER_ON_CNN_APPROACHES_FOR_POISONOUS_MUSHROOM_IDENTIFICATION_ijariie25516.pdf (Accessed : )
IEEE Kelvin Dmello , B S Sumukha , Akash Devadiga , Sooraj, and Charan S V, "A REVIEW PAPER ON CNN APPROACHES FOR POISONOUS MUSHROOM IDENTIFICATION," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 6, pp. 1807-1812, Nov-Dec 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/A_REVIEW_PAPER_ON_CNN_APPROACHES_FOR_POISONOUS_MUSHROOM_IDENTIFICATION_ijariie25516.pdf [Accessed : ].
Turabian Kelvin Dmello , B S Sumukha , Akash Devadiga , Sooraj, and Charan S V. "A REVIEW PAPER ON CNN APPROACHES FOR POISONOUS MUSHROOM IDENTIFICATION." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 6 ().
Vancouver Kelvin Dmello , B S Sumukha , Akash Devadiga , Sooraj, and Charan S V. A REVIEW PAPER ON CNN APPROACHES FOR POISONOUS MUSHROOM IDENTIFICATION. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(6) : 1807-1812. Available from: https://ijariie.com/AdminUploadPdf/A_REVIEW_PAPER_ON_CNN_APPROACHES_FOR_POISONOUS_MUSHROOM_IDENTIFICATION_ijariie25516.pdf
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