How video and image annotation will reduce waste management in the food industry?
Waste management in the food industry is a major source of global concern. As we delve deeper in this article, we are going to explore how waste is being reduced to its barest minimum through video and image annotations.
It is the act of labelling an image in a training model for the purpose of training the machine models. The image annotation process begins with a manual annotation where the images are labelled. They are further processed by a machine learning model.
It is the process of capturing various images in a video, with the use of frame-by-frame annotated lines. It makes it easy for the machines to recognise moving objects.
It is the action taken that aims to reduce food wastage and its associated implications on the environment. Food waste may be in the form of packaging, solid or liquid.
Many humanitarian agencies and food banks use image and video annotations to map out vehicle routes to reach out to people and families in dire need of food aid (which is usually donated by those with excess food) , probably because of a natural disaster or an ongoing armed conflict. Convenient distribution points are identified with GPS signals rather than having to stop sometimes to reach out to these people. Satellite images also take specific locations where there is a food crisis or difficulty in getting enough food by inhabitants.
It is the process where microorganisms such as fungi and bacteria break down organic waste ,including food, into smaller forms. In addition, they can be added to soil as fertilizer for plant growth Waste classification is a method which uses Convolutional Neural Networks (CNN) to develop models of waste image classification. Mobile apps have been developed to aid in garbage identification,after garbage images are annotated for the training models. Monitors and sensors have been used to view and track the composting process. CNN also identifies the early maturity compost, through the acquisition of samples under various levels of illumination for analysis of its development stages
Robots are being used to sort and grade healthy leftover food or crops for animals to feed on through the use of machine learning models and annotated images.They detect existing defects of crops to know how long they will remain to feed the livestock. Video and image annotations are also helping robots to sort different parts of plants such as stems. They detect the plant breed suitable for feeding particular livestock.
It is a chemical process which breaks down organic waste into simple components through bacteria activity. Included in the broken down organic waste is food. The waste is then produced in the absence of oxygen and metamorphosed to combustible biogas( made up of carbon dioxide and methane) in Anaerobic digestion soft sensors estimate the production of volatile fatty acids(VFA) online under various working conditions. Artificial Neural Networks make use of Radial Basis Function and Back Propagation Neural Networks which use algorithms to update network weight and also estimate functions which clear uncertainties in the digestion process.
A lot of plastic waste is produced by the food and drinks packaging industry. Video and image annotations train models to recognise different types of waste produced by the food industry. Included are single use plastic and cans. Once recognised, the various forms of waste are easily separated for the various purposes they can serve.Plastic bottles have been used for nursing seedlings ,making backpacks, umbrellas and pencil cases. They are sometimes even used for brick and road construction. Food cans, on the other hand are used as piggy banks and for bulk water storage in certain rural communities, among others.
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