Artificial Intelligence and Supervised Learning based Solid Waste Segregation Mechanism using ESP 32

Garbage separation must take place at the household level because it is time-consuming and frequently unsuccessful to do it at the disposal. This Proposed method attempts to decrease mannual effort in segregation of waste in order to achieve proper waste segregation with the least amount of effort. This proposed system suggests an automatic trash segregator system powered by IOT. In addition to detecting and classifying household rubbish into dry, moist, and metallic waste streams, it aids in real-time monitoring the levels of garbage in trash discharges. A metallic sensor is then used to determine whether there is any metal in the garbage after an Ultrosonic [radar based] has detected the arrival of the trash. Additionally, the trash is separated using the capacitive detector into dry and moist residue. The conveyor helps to move the waste to the proper bins before the platform rotates once it has been detected and segregated using a supervised learning-based algorithm. Additionally, the system uses ESP 32 to display a note to clean the trash can when it is occupied and to show the amount of compost in the trash discharges on the LCD screen. Experimental results show that the proposed method is effective in categorizing waste into moist and dry waste as well as metal and non-metal waste.