Abstract
In the world, a huge amount of food is produced every day for the consumption of all living organisms. However, around one-third of the food produced is wasted every day. One of the reasons for the wastage of food is the lack of knowledge among the people regarding the state of the food if it is fresh or rotten. Here, we aimed to find out a way to reduce the wastage of food caused due to the mentioned reason. We developed a tool FreshYolo which uses powerful machine learning algorithms like YOLOv4 to build a model which tells us if the vegetable is fresh or rotten. This model was also compared to another model which was built using YOLOv3 and the results showed that the model built on YOLOv4 is better than the one built on YOLOv3 as the mAp of YOLOv4(0.7863) was better than the mAp of YOLOv3(0.7433). In conclusion, our comparison between YOLOv4 and YOLOv3 shows that YOLOv4 is more suitable for our tool FreshYolo which will help the people to know if the vegetable is fresh or rotten; thus, we can reduce the amount of food being wasted across the globe.
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Sanghi, P., Panda, S.K., Pati, C., Gantayat, P.K. (2022). Learning Deep Features and Classification for Fresh or off Vegetables to Prevent Food Wastage Using Machine Learning Algorithms. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_44
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DOI: https://doi.org/10.1007/978-981-16-6624-7_44
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