Published Paper Details:

Automating fruit classification with computer vision is a promising application in agriculture. Traditional methods rely on manual visual inspection, which can be tedious, time-consuming, and inconsistent. Typically, fruit classification has been based on their outer shape. However, recent advancements in computer vision and imaging technologies have proven to be more effective in the fruit industry, particularly for quality control in terms of color, size, and shape. Research indicates that machine vision systems can enhance product quality and reduce the need for manual sorting. This article explores various imaging techniques used in fruit classification. Fruits and vegetables are crucial sources of nutrition worldwide. As the global population grows, agricultural enterprises aim to minimize product losses and improve quality and productivity. Therefore, farmers are increasingly adopting advanced technologies for sustainable, eco-friendly, and efficient agriculture. Intelligent agriculture, focusing on early detection and disease control, is a key research area in the fruit sector. Precision agriculture, which integrates technologies such as machine learning, deep learning, and the Internet of Things (IoT), is transforming the industry. Current research primarily investigates the impact of these technologies on specific fruit and vegetable species.