Improving Apple Fruit Quality Detection with AI and Machine Vision

Abstract

The detection of apples using Raspberry Pi is an innovative approach that merges the realms of computer vision, machine learning, and agricultural automation. This abstract provides an extensive overview of the methodologies, implementations, challenges, and future directions pertaining to apple detection using Raspberry Pi, encapsulating the essence of the research conducted in this domain. The quest for automation in agriculture has spurred the development of novel technologies aimed at improving efficiency and reducing manual labor. Fruit detection, particularly the identification of apples, holds significant importance due to the fruit's widespread cultivation and economic value. Traditional methods of fruit detection often involve manual sorting, which is labor>intensive and time>consuming. Hence, there arises a need for automated systems capable of accurately identifying and sorting fruits, thereby streamlining agricultural processes. The implementation section details the practical realization of the apple detection system using Raspberry Pi. Hardware setup involves the integration of Raspberry Pi boards with camera modules and other peripherals necessary for image acquisition and processing. Software development entails the creation of Python>based modules for image preprocessing, feature extraction, and classification. OpenCV and scikit>learn libraries are utilized for implementing image processing and machine learning algorithms, respectively. The system is tested in different environments to evaluate its performance under various conditions, including controlled laboratory settings and outdoor agricultural scenarios.