Abstract
Research findings on artificial intelligence are discussed in a new report. According to news reporting out of Mbarara, Uganda, by NewsRx editors, research stated, "Automation improves the quality of fruits through quick and accurate detection of pest and disease infections, thus contributing to the country's economic growth and productivity." Financial supporters for this research include Ademnea Project Under Norhed.. Our news reporters obtained a quote from the research from Mbarara University of Science and Technology: "Although humans can identify the fruit damage caused by pests and diseases, the methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits affect their market value. The issue of pest and disease infections further deteriorates fruits' quality, becoming a mounting stressor on farmers since they reduce the potential revenue from fruit production, processing, and export. This article reviews various studies on detecting and classifying damages in fruits. Specifically, we review articles where state-of-the-art approaches under segmentation, image processing, machine learning, and deep learning have proved effective in developing automated systems that address hurdles associated with manual methods of assessing damage using visual experiences. This survey reviews thirty-two journal and conference papers from the past thirteen years that were found electronically through Google Scholar, Scopus, IEEE, ScienceDirect, and standard online searches."
NETL
NSTL
IEEE