Impact of Artificial Intelligence and Machine Learning in the Food Industry: A Survey

Introduction

Technology is an essential component of food manufacturing and distribution in today's food industry. Packaging might be considerably improved with technology, extending shelf life and boosting food safety. Other technologies, such as machines, drones, robotics, and 3D printing, improve food quality and also cut down manufacturing costs of a food product. AI and ML technologies deal with a variety of ways to expedite and process automation, save revenue, remove human mistakes, reduce waste of plentiful items, happier consumers, streamlined and automated operations, and more individualized orders in a variety of food businesses such as restaurants, bars, cafes, and food manufacturers. In production lines, AI systems exceed human efforts in terms of accuracy, speed, and consistency. Artificial Intelligence solutions have much potential for enhancing hygiene and cleaning activities, which are the most critical variables in food safety. Intelligent algorithms can help businesses enhance the quality of their food and services, resulting in healthier meals for customers. AI makes more efficient use of enormous quantities of comprehensive agricultural data for our food crops to record levels of productivity. While AI has a lot of benefits in the food sector, it also has many disadvantages. AI has failed to become widespread due to financial constraints and a scarcity of experienced specialists. The industry's market growth is being stifled by the increased cost of large-scale implementation. It's never easy to integrate new technologies like AI and ML in its early stage of deployment into the food industry. Humans will always be needed to supervise operations, repair, and maintain old equipment in the food industry. Therefore AI will never be able to replace them. Technology can effectively collaborate with humans to boost operational efficiency. Plant pests and diseases, which vary from place to region and season to season, are a major concern in the food sector (FAO, April 2017). AI algorithms have a significant impact on predicting plant diseases. Machine Learning encompasses deep learning as a subpart (Figure 1). It simulates how humans make judgments using machine learning and neural networks (Zhang et al., 2021). It is pretty expensive and demands a considerable number of data points.

Figure 1.

AI, ML, and DL relationships

The Machine Learning Procedure

Data is collected by a computer, which allows systems to learn new things from it. For the system, ML creates new self-learning algorithms and makes predictions as shown in figure 2. Machine learning employs statistics to detect patterns in large amounts of data. Essentially, anything that can be converted to digital data, such as numbers, images, and clicks, can be put into a sophisticated Machine Learning algorithm. Machine Learning is one of the essential technologies on the planet right now. Netflix, Spotify, and YouTube may now be able to provide suggestions. There are numerous other ML instances, such as feeds from multiple social media sites like Twitter and Facebook or voice assistant programs such as Siri and Alexa.

Figure 2.

An ML model

Machine learning techniques are classified into two types: supervised learning (Parvin et al., 2013) and unsupervised learning (Minaei-Bidgoli et al., 2014).

Supervised Learning - To train computers, this method employs examples of labeled data. In supervised learning, classification is a common technique Rokach, 2010). Neural networks, support vector machines, and decision trees are examples of classification techniques (Parvin et al., 2015).

Unsupervised Learning - This method allows algorithms to recognize patterns in data and group them into groups based on similarities. The K-means clustering method (Ahmad et al., 2007) is the most widely used clustering algorithm.