Automatic Protection of Clothes From Rain
EasyChair Preprint 12341
36 pages•Date: March 1, 2024Abstract
The project "Tender Coconut Type Detection and Classification System using TCNN and
also find the glucose level of the tender coconut using Laplacian of Gaussian (LoG) Edge Detection"
aims to develop an automated system for detecting and classifying different types of tender coconuts
based on their external characteristics using a combination of the Laplacian of Gaussian (LoG) edge
detection algorithm and TCNN, and provide a recommendation for the appropriate glucose level
based on the estimated size.The TCNN model will be used to classify the tender coconuts into
different types based on their external characteristics. Laplacian of Gaussian (LoG) edge detection
algorithm will also be developed to estimate the size of tender coconuts based on their external
characteristics, such as diameter, height, and weight. The Laplacian of Gaussian (LoG) edge
detection algorithm will be used to estimate the glucose level of the tender coconut based on the
images of the external characteristics. A recommendation system will be developed to provide
guidance on the appropriate glucose level based on the estimated size of the tender coconut and
established standards and guidelines.
Keyphrases: Agricultural Technology, Biometrics, Coconut Classification, Convolutional Neural Networks (CNNs), Ethical AI, Healthcare Applications, Transfer Learning, computer vision, data preprocessing, data privacy, deep learning, feature engineering, food science, glucose prediction, image classification, machine learning, model integration, neural networks, predictive modeling, regression analysis