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RISS 인기검색어
- 검색키워드
- 저자 : ( Yogesh Chawla ) (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Rathore Divya,Divyanth L. G.,Reddy Kaamala Lalith Sai,Chawla Yogesh,Buragohain Mridula,Soni Peeyush,Machavaram Rajendra,Hussain Syed Zameer,Ray Hena,Ghosh Alokesh 한국농업기계학회 2023 바이오시스템공학 Vol.48 No.2
Purpose The process of robotic harvesting has revolutionized the agricultural industry, allowing for more effi cient and costeff ective fruit picking. Developing algorithms for accurate fruit detection is essential for vision-based robotic harvesting of apples. Although deep-learning techniques are popularly used for apple detection, the development of robust models that can accord information about the fruit’s occlusion condition is important to plan a suitable strategy for end-eff ector manipulation. Apples on the tree experience occlusions due to leaves, stems (branches), trellis wire, or other fruits during robotic harvesting. Methods A novel two-stage deep-learning-based approach is proposed and successfully demonstrated for detecting ontree apples and identifying their occlusion condition. In the fi rst stage, the system employs a cutting-edge YOLOv7 model, meticulously trained on a custom Kashmiri apple orchard image dataset. The second stage of the approach utilize the powerful Effi cientNet-B0 model; the system is able to classify the apples into four distinct categories based on their occlusion condition, namely, non-occluded, leaf-occluded, stem/wire-occluded, and apple-occluded apples. Results The YOLOv7 model achieved an average precision of 0.902 and an F1-score of 0.905 on a test set for detecting apples. The size of the trained weights and detection speed were observed to be 284 MB and 0.128 s per image. The classifi cation model produced an overall accuracy of 92.22% with F1-scores of 94.64%, 90.91%, 86.87%, and 90.25% for nonoccluded, leaf-occluded, stem/wire-occluded, and apple-occluded apple classes, respectively. Conclusion This study proposes a novel two-stage model for the simultaneous detection of on-tree apples and classify them based on occlusion conditions, which could improve the eff ectiveness of autonomous apple harvesting and avoid potential damage to the end-eff ector due to the objects causing the occlusion.
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( Sunil Taneja ),( Ajay Duseja ),( Arka De ),( Vivek Kumar ),( Raja Ramachandran ),( Ashish Sharma ),( Radha K Dhiman ),( Krishan L Gupta ),( Yogesh Chawla ) 대한간학회 2017 춘·추계 학술대회 (KASL) Vol.2017 No.1
Background and Aims: The data regarding the treatment of chronic hepatitis C (CHC) in renal transplant recipients is lacking from the Asia-Pacific region. Aim of the present study was to assess the safety and efficacy of directly acting antivirals (DAAs) in the treatment of CHC infection in renal transplant recipients. Methods: A total of 47 HCV infected renal transplant recipients were enrolled in this real life observational cohort analysis between March 2015 and September 2016. Presence of hepatic fibrosis/cirrhosis was assessed on transient elastography (Fibroscan). Fourteen patients were treated with Sofosbuvir and Ribavirin for 24 weeks. Twenty-two patients received Sofosbuvir and Ledipasvir and twelve patients received Sofosbuvir and Daclatasvir with (n=3) or without (n=31) Ribavirin for 12 or 24 weeks depending on genotype and underlying cirrhosis. Data was analyzed for safety and treatment efficacy [sustained virological response at 12 weeks (SVR12)]. Results: The mean baseline HCV RNA concentration in the whole group was 7.38 x 106 IU/ml (1.23 x 104- 6.36 X 107). The SVR12 rates were 100% in all groups except in the Sofosbuvir and Ribavirin group (86%). Transient Elastography revealed minimal or no fibrosis (F0-F1) in 31 (65.96%) patients, moderate fibrosis (F2) in 11 (23.4%) patients and cirrhosis in 5 (10.64%) patients. The only serious adverse effect was anemia observed in 8 (57%) patients in the Sofosbuvir and Ribavirin group. Conclusions: DAAs including Sofosbuvir, Daclatasvir and Ledipasvir with or without ribavirin are safe and effective for the treatment of chronic hepatitis C in renal transplant recipients
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