Convolutional Neural Network for Solid Waste Segregation and Management - P.Pandiaraja, Mr.S.Santhana Hari, S.Suriya, S.Karthikeyan (International Journal of Advanced Science and Technology, 2020)
Garbage, a solid material that is dumped in billions of tons a very year. About a lakh of one metric of wastes is dumped every day in some countries like India. This is due to the increase in the productivity of goods and all object matters that concern day to day use. We, as a consumer will collect the necessary materials and throw away the wastes into open lands or in some water bodies, sometimes even on the roadsides. Since the segregation of waste materials which are useful and useless items seems to be a tedious task for humans at home. They simply throw away the waste materials into the garbage can or land areas, and in turn, all these wasteland areas are filling rapidly. This is all due to the production of materials for meeting the demands of the exponentially growing world’s population. Though the production of these plastic/packing materials can be avoided to some sought of the amount. The real matter tells about the segregation and recycling of these materials. The solid waste materials which are left untreated or uncollected will definitely cause degradation to the land areas, water bodies and much more pollution to the environment. Even though we are collecting all the waste materials regularly, it is really a tuff job to segregate waste materials. With the help of manpower, we can able to segregate these materials, but how quick and efficient it can be done when comparing with the automatic detection of waste materials and segregation propose. This automatic segregation of waste will also help in reducing the cost of labor wage, in terms of business productivity. More the segregation of waste from trivial waste will helpful for the recycling process. The process of recycling can able to reduce the production of new solid substances. Overall proposed work is done successfully, for the efficient classification of solid waste matters and had achieved good accuracy.