Enhanced rendering-based approach for improved quality of instance segmentation in detecting green gram (Vigna Rediata) pods

Abstract

The emergence of Artificial Intelligence, deep learning, and current computer vision algorithms are the main contributors to innovations in the agricultural domain. The most recent detection algorithms capable of giving real-time detections at the edge nodes tackle most agricultural problems, such as disease, pest or insect de tections, and maturity level detection of crops (fruits and vegetables). Modern harvesters and fruit-picking robots rely heavily on the detection capability of the algorithms used. Various detection algorithms have been proposed and used in literature, having good performance in terms of mean average precision. Still, the current agricul tural systems require not only high mean average accuracy but also algorithms should have high inference speeds. The research proposes a Detectron2-based framework with PointRend (Point-based Rendering), capable of providing enhanced, high-quality pixel-level instance segmentation in identifying and detecting green gram pods or Mung Bean (Vigna Radiata) in natural field conditions rendering crisp and smooth boundaries for accurately locating the green gram pods. The results indicate that the proposed framework outperforms the famous Mask R-CNN model to obtain higher mean average precision and improved quality of detections.