Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

Abstract

Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.

A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector

“…Over the past few years, the ImageNet dataset [8], consisting of a large number of images, has significantly enhanced the precision of research utilizing neural networks [4], [16], [17] for the purposes of image categorization and object recognition. In the future, the release of the COCO database, which aims to identify non-iconic objects, could enable researchers to do more precise object recognition, instance segmentation, image captioning, and human keypoint localization [14].…”

“…The detection of fruit has been undertaken by researchers with a broad spectrum of sensor technologies and algorithms; however, cameras and computer vision techniques are the most effective combination [8]. Unfortunately, using computer vision technology in outdoor orchard settings comes with its challenges, including the following: i) varying brightness conditions and ii) occlusion of fruits by other leaves, branches, or other fruits.…”