Intelligent organic recyclable objects classification system using machine learning for landfill minimization

The issue of waste management and landfill minimization has become increasingly critical, particularly in India, where urbanization and consumption rates have significantly risen. With rapid urban growth, the waste generation in India has reached alarming levels. According to the Central Pollution Control Board (CPCB), India generates over 62 million tons of waste annually, and the majority of it is not recycled. The Intelligent Organic Recyclable Objects Classification System aims to classify waste into organic and non-organic categories using machine learning models, enabling better waste management practices. The objective of this system is to develop a machine learning-based classification model to identify organic and non-organic waste for efficient recycling and landfill reduction, minimizing environmental impact. Traditionally, waste segregation has been done manually by workers at landfills or recycling facilities. Before the adoption of machine learning or AI, waste classification relied heavily on manual sorting, leading to inefficiencies, human errors, and inconsistent separation of waste types. Sorting processes involved manual labor, which is time-consuming, prone to errors, and inefficient. The motivation behind this research is to address the challenges posed by manual waste segregation and to promote sustainable waste management practices. With increasing waste generation and limited recycling efforts in India, there is an urgent need for automated systems that can classify waste efficiently and reduce landfill burden. The proposed system utilizes machine learning algorithms to automate waste classification, distinguishing between organic and non-organic objects. By using datasets with labeled examples of both organic (e.g., fruits) and non-organic waste (e.g., plastics, paper), the system can be trained to identify and classify waste with high accuracy. This AI-powered approach significantly reduces human labor, minimizes errors, improves sorting efficiency, and accelerates recycling processes, leading to less waste in landfills and contributing to environmental sustainability.