MACHINE LEARNING FOR IMAGE CLASSIFICATION IN INDUSTRIAL APPLICATIONS: A REVIEW OF TECHNIQUES AND CASE STUDIES

In industrial applications, image classification is a vital task, such as object recognition and quality control. Due to the increasing popularity of machine learning techniques, it has become possible to automate this process. Classification of images is an essential part of industrial workflows, particularly in sectors such as healthcare, manufacturing, and agriculture. Machine learning has made image classification more accurate and enables the automation of manual processes. The paper reviews the various techniques used in image classification for industrial applications. It focuses on CNNs, RNNs, and ANNs. The paper explores the different algorithms that are used in image classification and their disadvantages and advantages. It also covers the latest developments in machine learning. This paper presents a case study about machine learning for the identification of tomato leaf diseases. Through a CNN, it was able to classify images of the plant as either healthy or diseased. Its findings show how effective CNNs are in identifying plant diseases and how they can be utilized to automate the detection of such diseases. The paper discusses that machine learning is vital in image classification, as the use of CNNs, ANNs, and RNNs can result in significant improvements in its efficiency and accuracy. It also emphasizes the need for more extensive datasets and the creation of more advanced models.