Dual Attention Fusion-Net with Edge Attention Guidance Network based Segmentation for an Automatic Size Detection of Onions

Onion size is a crucial physiological characteristic that can be explained by a number of factors, including diameter, weight, volume, and length. Determining the size of onions is frequently necessary for sorting them for a variety of reasons, including processing machine specifications, legal requirements for sorting standards, and consumer preferences. In the process of phenotyping onions, size is another crucial quantitative feature to consider. Traditionally, algorithms based on morphology, colour, thresholding, and geometric approaches have been used to estimate the shape and size of onions. However, research that relies on these geometric or colour-based functions is limited to approximations and frequently produces erroneous results when conducted at precisely controlled heights. Healthy onions are collected and utilized as an input dataset for this paper. The gathered images are pre-processed to reduce noise and improve contrast by applying the circular adaptive median filter and homomorphic filtering with Elk-herd optimization. Next, utilizing the dilated and deformable feature pyramid network, object detection is performed on the pre-processed images. To segment the onion from the image for removing the unwanted portions, an edge-based segmentation algorithm is used, such as an edge-attention guidance network. The dual attention fusion-net, which ranks data into labelled groups and measures onion size. Accuracy, confusion metrics, FDR, hit rate, and other performance metrics are assessed for both the current and proposed models in the proposed model. Consequently, the suggested onion size detection approach outperforms the current algorithm. This method produced 97.6% accuracy, 2.9% FDR, 96% Hit Rate, 98.5% Selectivity, and 97.3% NPV. Thus, this proposed approach is the best choice for detecting the size of the onion.