INTELLIGENTORGANICRECYCLABLEOBJECTSCLASSIFICATION SYSTEM USING AI FOR LANDFILL MINIMIZATION

Abstract

Because of the dramatic increase in
both urbanization and consumption
rates, the problem of waste
management and the reduction of
landfill use has assumed paramount
importance, especially in India. The
degree of garbage produced in India
has reached worrying heights due to
the country's fast urbanization. The
bulk of India's 62 million tones of trash
is not recycled, according to the
Central Pollution Control Board
(CPCB). To improve garbage
management, the Intelligent Organic
Recyclable Objects Classification
System use machine learning models to
sort trash into organic and non-organic
groups. In order to reduce landfill use
and maximize recycling efficiency
while keeping environmental impacts
to a minimum, this system aims to
create a classification model that uses
machine learning to distinguish
between organic and non-organic trash.
Employees at landfills and recycling
centres have historically separated
trash by hand. Humans used to sort
garbage by hand, which was slow,
prone to mistakes, and didn't always
separate different kinds of trash. This
was before machine learning and AI
were widely used. Traditional sorting
methods relied on labor-intensive,
error-prone, and wasteful physical
labor. Addressing the issues of manual
trash segregation and promoting
sustainable waste management
methods are the motivations driving
this study. Automated systems that can
sort garbage effectively and lessen the
load on landfills are desperately needed
in India, where garbage production is
on the rise but recycling efforts are
low. The suggested method achieves
remarkable accuracy in trash
classification by using a Convolutional
Neural Network (CNN). In a
convolutional neural network (CNN),
spatial feature extraction occurs in
convolutional layers; non-linearity is
addressed by ReLU activations;
dimensionality reduction occurs in
pooling layers; and classification is
performed by fully connected layers.
In order to train the model, we use a
tagged dataset of trash photos and data
augmentation methods including
normalization, rotation, and flipping to
improve generalization, along with
categorical cross-entropy loss and the
Adam optimizer