The Role of Machine Learning and Computer Vision in the Agri-Food Industry

Machine Learning

Machine learning's three major goals are supervised machine learning, unsupervised machine learning, and reinforcement learning. A predictive model is constructed via supervised learning by integrating labeled data with prior knowledge of the input and expected output variables. Supervised learning's purpose is to convert the input parameters to the required output variables. Approaches to supervised learning include decision trees, Bayesian networks, and regression analysis. Unsupervised learning methods are used on unlabeled datasets with uncertain dependent and independent variables. Unsupervised machine learning, which is often used for data reduction and exploratory analysis, creates hidden patterns from an unlabeled dataset. In reinforcement learning, the training and testing datasets are combined, and the learner interacts with the environment to obtain data. The learner is rewarded for his interactions with the environment, equating to exploration and exploitation. In opposed to applying previously acquired knowledge, the learner must investigate newly unknown activities to get extra information. As a result, Machine Learning (ML) is a vital component of artificial intelligence, as one of the characteristics of intelligence is the capacity to learn from its environment. Machine learning algorithms, in general, synthesize knowledge from unstructured data in such a manner that the resulting software programs (i.e., expert systems) are capable of performing valuable tasks. The Figure 1 shows the structure of AI, Machine learning and Deep Learning.

Figure 1.

Structure of AI

The Figure2 shows the structure of the types of Machine learning and Deep Learning algorithms used in this sector.

Figure 2.

Types of ML algorithms