The food for healthy living, but even with this awareness we a lagging in controlling the food getting wasted every day. It was reported that about 1/3^rd of the total food produced in a year is wasted globally, which is an alarming figure and proper measures have to be taken to reduce the waste. In this work we propose a sustainable food waste management system in a college hostel environment, for which we propose a Smart E-Bin, which is connected with sensors such as ultrasonic sensor, Load cell and Mems Accelerometer for collecting the bin parameters and for executing the communication task the smart bin is connected with a GSM module to send the bin overflow level to the domestic worker through SMS. The proposed system also have the connectivity to internet which is catered with the help of ESP32 module through which the data was collected in college web server for about 3 months and simultaneously displayed in the display kept in the mess hall through Bluetooth. With the help of Machine Learning the collected data was analyzed using ARIMA model and suggestions were given on changing the menu of particular meal in a week on which the food waste was in huge quantity on a routine basis for the days it was monitored. The performance of this proposed system is analyzed by comparing the quantity of food waste predicted by the model and the actual food waste. Experimental results shows and a huge amount of food waste is reduced through this sustainable food waste management system. The system is implemented with Arduino microcontroller connected with pic microcontroller for transmission and the system was developed using Arduino IDE and Micro C. the analysis part is implemented using python programming.

用于健康生活的食物，但是即使有了这种认识，我们仍然在控制每天浪费食物方面仍然落后。据报道，约1/3 ^RD全球一年中浪费掉的粮食中有90％被浪费掉了，这是一个令人震惊的数字，必须采取适当的措施来减少浪费。在这项工作中，我们提出了一种在大学宿舍环境中的可持续食物垃圾管理系统，为此我们提出了一个智能电子垃圾箱，该电子垃圾箱与超声波传感器，称重传感器和Mems加速度计等传感器相连，用于收集垃圾箱参数并执行智能仓与GSM模块连接以执行通信任务，以通过SMS将仓溢出级别发送给家庭工人。拟议的系统还具有到Internet的连接性，借助ESP32模块可以满足该要求，通过该模块在大学Web服务器中收集了大约3个月的数据，并同时通过蓝牙将其显示在食堂的显示器中。在机器学习的帮助下，使用ARIMA模型对收集的数据进行了分析，并提出了在一周内更改特定餐点菜单的建议，在该周中，日常日常监控期间会大量浪费食物。通过比较模型预测的食物垃圾数量和实际食物垃圾数量，分析了该提议系统的性能。实验结果表明，通过这种可持续的食物垃圾管理系统可以减少大量的食物垃圾。该系统由与PIC微控制器连接的Arduino微控制器实现传输，并且该系统是使用Arduino IDE和Micro C开发的。分析部分是使用python编程实现的。