Enhanced rendering-based approach for improved quality of instance segmentation in detecting green gram (Vigna Rediata) pods
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 a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375523002137 |
| _version_ | 1797246788396122112 |
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| author | Nagaraj V. Dharwadkar RajinderKumar M. Math |
| author_facet | Nagaraj V. Dharwadkar RajinderKumar M. Math |
| author_sort | Nagaraj V. Dharwadkar |
| collection | DOAJ |
| description | 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 detections, 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 agricultural 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. |
| first_indexed | 2024-03-08T21:46:59Z |
| format | Article |
| id | doaj.art-471ec3af15ac4f668c14476e363618d3 |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-04-24T19:48:22Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-471ec3af15ac4f668c14476e363618d32024-03-25T04:18:11ZengElsevierSmart Agricultural Technology2772-37552024-03-017100386Enhanced rendering-based approach for improved quality of instance segmentation in detecting green gram (Vigna Rediata) podsNagaraj V. Dharwadkar0RajinderKumar M. Math1Department of Computer Science, School of Computer Science, Central University of Karnataka, Kalaburagi, India; Corresponding author.Department of Electronics and Communication Engineering, B.L.D.E. Association's V.P Dr. P.G. Halakatti College of Engineering and Technology, Ashram Road, Vijayapur, IndiaThe 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 detections, 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 agricultural 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.http://www.sciencedirect.com/science/article/pii/S2772375523002137Object detectionDetectron2PointrendGreen gram podsInstance segmentation |
| spellingShingle | Nagaraj V. Dharwadkar RajinderKumar M. Math Enhanced rendering-based approach for improved quality of instance segmentation in detecting green gram (Vigna Rediata) pods Smart Agricultural Technology Object detection Detectron2 Pointrend Green gram pods Instance segmentation |
| title | Enhanced rendering-based approach for improved quality of instance segmentation in detecting green gram (Vigna Rediata) pods |
| title_full | Enhanced rendering-based approach for improved quality of instance segmentation in detecting green gram (Vigna Rediata) pods |
| title_fullStr | Enhanced rendering-based approach for improved quality of instance segmentation in detecting green gram (Vigna Rediata) pods |
| title_full_unstemmed | Enhanced rendering-based approach for improved quality of instance segmentation in detecting green gram (Vigna Rediata) pods |
| title_short | Enhanced rendering-based approach for improved quality of instance segmentation in detecting green gram (Vigna Rediata) pods |
| title_sort | enhanced rendering based approach for improved quality of instance segmentation in detecting green gram vigna rediata pods |
| topic | Object detection Detectron2 Pointrend Green gram pods Instance segmentation |
| url | http://www.sciencedirect.com/science/article/pii/S2772375523002137 |
| work_keys_str_mv | AT nagarajvdharwadkar enhancedrenderingbasedapproachforimprovedqualityofinstancesegmentationindetectinggreengramvignarediatapods AT rajinderkumarmmath enhancedrenderingbasedapproachforimprovedqualityofinstancesegmentationindetectinggreengramvignarediatapods |