Real-Time Vegetables Recognition System based on Deep Learning Network for Agricultural Robots

Zheng, Yurong; Kong, Jianlei; Jin, Xin; Su, Tingli; Nie, Ming-Jun; Bai, Yuting

doi:10.1109/cac.2018.8623610

Cited by 12 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zheng at al. (Zheng et al, 2018) compared different object detectors for identifying vegetable crops.…”

Section: Discussionmentioning

confidence: 99%

“…YOLOv2 and RetinaNet can perform faster than Faster R-CNN because they implemented a single stage detection process. Additionally, they showed higher performance on standard dataset (Kamilaris & Prenafeta-Boldú, 2018;Zheng et al, 2018). However, Faster R-CNN has been a popular method for several applications due to ease of use (Kamilaris & Prenafeta-Boldú, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Agricultural applications of object detection are becoming more common. A real time vegetable detection system was developed using deep learning networks (Zheng et al, 2018). Multiple object detectors were selected in order to recognize tomato and cucumber at different stages.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Object Detection Methods for Corn Damage Assessment Using Deep Learning

HamidiSepehr¹,

Mirnezami²,

Ward³

2020

Transactions of the ASABE

View full text Add to dashboard Cite

HighlightsCorn damage detection was possible using advanced deep learning and computer vision techniques trained with images of simulated corn lodging.RetinaNet and YOLOv2 both worked well at identifying regions of lodged corn.Automating crop damage identification could provide useful information to producers and other stakeholders from visual-band UAS imagery.Abstract. Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions. The goal of this study was a proof-of-concept to detect areas of damaged corn from aerial imagery using computer vision and deep learning techniques. A specific objective was to compare existing object detection algorithms to determine which is best suited for corn damage detection. Simulated corn lodging was used to create a training and analysis data set. An unmanned aerial system equipped with an RGB camera was used for image acquisition. Three popular object detectors (Faster R-CNN, YOLOv2, and RetinaNet) were assessed for their ability to detect damaged areas. Average precision (AP) was used to compare object detectors. RetinaNet and YOLOv2 demonstrated robust capability for corn damage identification, with AP ranging from 98.43% to 73.24% and from 97.0% to 55.99%, respectively, across all conditions. Faster R-CNN did not perform as well as the other two models, with AP between 77.29% and 14.47% for all conditions. Detecting corn damage at later growth stages was more difficult for all three object detectors. Keywords: Computer vision, Faster R-CNN, RetinaNet, Severe weather, Smart farming, YOLO.

show abstract

“…Zheng at al. (Zheng et al, 2018) compared different object detectors for identifying vegetable crops.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Object Detection Methods for Corn Damage Assessment Using Deep Learning

HamidiSepehr¹,

Mirnezami²,

Ward³

2020

Transactions of the ASABE

View full text Add to dashboard Cite

show abstract

“…In the field [1] of detecting things in images, computer vision and pattern recognition are tools in this growing field. Object detection methods have numerous applications, including the detection of vegetables and fruits [2], [3]. The recent explosion in the capability of both AI algorithms and image sensor technology has led to the rise of the automated fruit detection system [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

“…Xu et al [21] studied a variation of faster R-CNN called mask R-CNN for counting cattle in real time. Zheng et al [22] obtained good performance using YOLOv3 for vegetable detection for an agricultural picking robot. Tian et al [23] compared the detection of YOLOv3 incorporated with a dense net method and faster R-CNN to detect apples in an orchard and plan to estimate yield in future work.…”

Section: Introductionmentioning

confidence: 99%

Automated Bale Mapping Using Machine Learning and Photogrammetry

2021

View full text Add to dashboard Cite

An automatic method of obtaining geographic coordinates of bales using monovision un-crewed aerial vehicle imagery was developed utilizing a data set of 300 images with a 20-megapixel resolution containing a total of 783 labeled bales of corn stover and soybean stubble. The relative performance of image processing with Otsu’s segmentation, you only look once version three (YOLOv3), and region-based convolutional neural networks was assessed. As a result, the best option in terms of accuracy and speed was determined to be YOLOv3, with 80% precision, 99% recall, 89% F1 score, 97% mean average precision, and a 0.38 s inference time. Next, the impact of using lower-cost cameras was evaluated by reducing image quality to one megapixel. The lower-resolution images resulted in decreased performance, with 79% precision, 97% recall, 88% F1 score, 96% mean average precision, and 0.40 s inference time. Finally, the output of the YOLOv3 trained model, density-based spatial clustering, photogrammetry, and map projection were utilized to predict the geocoordinates of the bales with a root mean squared error of 2.41 m.

show abstract

Real-Time Vegetables Recognition System based on Deep Learning Network for Agricultural Robots

Cited by 12 publications

References 4 publications

Comparison of Object Detection Methods for Corn Damage Assessment Using Deep Learning

Comparison of Object Detection Methods for Corn Damage Assessment Using Deep Learning

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

Automated Bale Mapping Using Machine Learning and Photogrammetry

Contact Info

Product

Resources

About