Assessing Tomato Maturity: CNN vs VGG16

被引：1
|
作者：
Maged, Salma [1 ]
Adel, Aya [2 ]
Tawfik, Mohamed [1 ]
Badawy, Wael [1 ]
机构：
[1] Egyptian Russian Univ, Sch Artificial Intelligence, Dept Data Sci, Cairo, Egypt
[2] Egyptian Russian Univ, Sch Artificial Intelligence, Dept Artificial Intelligence, Cairo, Egypt
来源：
2024 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE AND SMART INNOVATION, ICMISI 2024 | 2024年
关键词：
Tomato Maturity; Machine Learning; Computer Vision; Agriculture assisted technology;
D O I：
10.1109/ICMISI61517.2024.10580489
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present a performance analysis of two techniques for detection tomato maturity. Detecting plant maturity from images inherits several challenges. We designed two different architectures: (1) a general Convolutional Neural Networks "CNN" architecture. And (2) a pretrained architecture with 16 layers "VGG16". The experiment uses a set of 2986 images and proof that the general CNN has a better performance due to its ability to accommodate several challenges.
引用
收藏
页码：136 / 139
页数：4
相关论文
共 50 条
  • [1] Comparative study of CNN, VGG16 with LSTM and VGG16 with Bidirectional LSTM using kitchen activity dataset
    Aparna, R.
    Chitralekha, C. K.
    Chaudhari, Shilpa
    PROCEEDINGS OF THE 2021 FIFTH INTERNATIONAL CONFERENCE ON I-SMAC (IOT IN SOCIAL, MOBILE, ANALYTICS AND CLOUD) (I-SMAC 2021), 2021, : 836 - 843
  • [2] Image Forgery Detection by CNN and Pretrained VGG16 Model
    Gupta, Pranjal Raaj
    Sharma, Disha
    Goel, Nidhi
    PROCEEDINGS OF ACADEMIA-INDUSTRY CONSORTIUM FOR DATA SCIENCE (AICDS 2020), 2022, 1411 : 141 - 152
  • [3] CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture
    Praharsha, Chittathuru Himala
    Poulose, Alwin
    Computers in Biology and Medicine, 2024, 180
  • [4] Kiwifruit detection in field images using Faster R-CNN with VGG16
    Song, Zhenzhen
    Fu, Longsheng
    Wu, Jingzhu
    Liu, Zhihao
    Li, Rui
    Cui, Yongjie
    IFAC PAPERSONLINE, 2019, 52 (30): : 76 - 81
  • [5] Deep Learning Model Based on VGG16 for Tomato Leaf Diseases Detection and Categorization
    Abden, Sofiane
    Bendjima, Mostefa
    Benkrama, Soumia
    PROGRAM OF THE 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND AUTOMATIC CONTROL, ICEEAC 2024, 2024,
  • [6] Flower Recognition Using VGG16
    Rahman, Md. Ashikur
    Laskar, Md. Saif
    Asif, Samir
    Imam, Omar Tawhid
    Reza, Ahmed Wasif
    Arefin, Mohammad Shamsul
    THIRD INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND CAPSULE NETWORKS (ICIPCN 2022), 2022, 514 : 748 - 760
  • [7] Detection of Choroidal Neovascularization (CNV) in Retina OCT Images Using VGG16 and DenseNet CNN
    M. S. Abirami
    B. Vennila
    K. Suganthi
    Sarthak Kawatra
    Anuja Vaishnava
    Wireless Personal Communications, 2022, 127 (3) : 2569 - 2583
  • [8] Detection of Choroidal Neovascularization (CNV) in Retina OCT Images Using VGG16 and DenseNet CNN
    Abirami, M. S.
    Vennila, B.
    Suganthi, K.
    Kawatra, Sarthak
    Vaishnava, Anuja
    WIRELESS PERSONAL COMMUNICATIONS, 2022, 127 (03) : 2569 - 2583
  • [9] Construction of VGG16 Convolution Neural Network (VGG16_CNN) Classifier with NestNet-Based Segmentation Paradigm for Brain Metastasis Classification
    Alshammari, Abdulaziz
    SENSORS, 2022, 22 (20)
  • [10] Detection of Driver Dynamics with VGG16 Model
    Aytekin, Alper
    Mencik, Vasfiye
    APPLIED COMPUTER SYSTEMS, 2022, 27 (01) : 83 - 88
12345