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Mohanty, D. K.
- Effect of Selected Parameters on Funnel Side Slope Angle for Smooth Dropping of Seedlings in Semi-Automatic Vegetable Transplanter
Abstract Views :601 |
PDF Views:0
Authors
Affiliations
1 Krishi Vigyan Kendra, Shamakhunta (Orissa), IN
2 Department of Farm Machinery and Power, College of Agricultural Engineering and Technology, Orissa University of Agriculture and Technology, Bhubaneshwar (Orissa), IN
1 Krishi Vigyan Kendra, Shamakhunta (Orissa), IN
2 Department of Farm Machinery and Power, College of Agricultural Engineering and Technology, Orissa University of Agriculture and Technology, Bhubaneshwar (Orissa), IN
Source
International Journal of Agricultural Engineering, Vol 7, No 2 (2014), Pagination: 318-322Abstract
Studies were conducted on a two row semi-automatic vegetable transplanter to determine the optimum funnel side slope angle in the feeding and metering mechanism at different seedling ages and different types of seedlings. The optimum side slope of the funnel has been decided by laboratory experimentation so that not a single seedling sticks to the funnel side after being dropped from the finger tray of the feeding mechanism. The experiments have been designed as a three Factors Complete Randomized Design with four replications. It was found that, at funnel side slope angle of less than 75° with the horizontal, the per cent of seedlings slipped into the drop tube decreased as the length of seedlings increased. At side angle of funnel of 75° and more, 100 per cent seedlings were slipped into the drop tube irrespective of crop and size of seedlings. Behaviours of different crops were found to be different below 75°. The crop with less foliar development such as chilli slipped more easily than other crops having more foliar development such as brinjal. So a funnel side slope angle of 75° has been taken as optimum to ensure cent per cent slippage of seedling from side of the funnel to the dropping tube for all crops under study.Keywords
Furrow Opener, Funnel, Finger Trays, Dropping Tube, Slope Angle.
References
- Anonymous (2011a). Indian Horticultural Database-2011. National Horticultural Board, Ministry of Agriculture, Government of India, NEW DELHI, INDIA.
- Craciun, V. and Balan, O. (2005). Technological design of a new transplanting machine for seedlings. J. Central Eur. Agric., 7(1): 164.
- Garg, I.K. and Dixit, A. (2002). Design, development and evaluation of vegetable transplanter. Paper presented during 24th Workshop of AICRP on FIM (ICAR) held at TNAU, Coimbatore on 18-21, April.
- Mahapatra, M. (2010). Design, development and evaluation of a power tiller operated vegetable transplanter. Ph.D. Thesis, Bidhan Chandra Krishi Vishavidhyalaya, Mohanpur, WEST BENGAL (INDIA).
- Narang, M.K., Dhaliwal, I.S. and Manes, G.S. (2011). Development and evaluation of a two row revolving magazine type vegetable transplanter. J. Agric. Engg., 48(3) : 1-7.
- Parish, R.L. (2005). Current developments in seeders and planters for vegetable crops. Hort. Technol., 15(2) : 1-6.
- Satpathy, S.K. (2003). Effect of selected parameters on the performance of vegetable transplanters. M. Tech. Thesis, Punjab Agricultural University, Ludhiana, PUNJAB (INDIA).
- Satpathy, S.K. and Garg, I.K. (2008). Effect of selected parameters on the performance of a semi-automatic vegetable transplanters. AMA, 39(2): 47-51.
- Tale, V.P., Taley, S.S., Revaskar, V.A. and Bhende, S.M. (2004). Testing, calibration and costing of semi-automatic vegetable transplanter. Paper presented in the proceedings of 38th ISAE convention held at the College of Agricultural Engineering and Technology, Dr. B.S. Konkan Krishi Vidyapeeth, Dapoli on January 16-18, India. pp: 92-98.
- An Improvised Video Enhancement Using Machine Learning
Abstract Views :295 |
PDF Views:1
Authors
Affiliations
1 Government B.Ed. Training College, Kalinga, IN
2 Department of Computer Science and Engineering, K.S.K College of Engineering and Technology, IN
3 Department of Electrical and Electronics Engineering, St. Peter Institute of Higher Education and Research, IN
4 Department of Computer Science, Vels Institute of Science Technology and Advanced Studies, IN
1 Government B.Ed. Training College, Kalinga, IN
2 Department of Computer Science and Engineering, K.S.K College of Engineering and Technology, IN
3 Department of Electrical and Electronics Engineering, St. Peter Institute of Higher Education and Research, IN
4 Department of Computer Science, Vels Institute of Science Technology and Advanced Studies, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2844-2848Abstract
The visual quality of underexposed videos can be increased with the help of a process known as video enhancement. Within the scope of this research, we present a novel approach to enhancing the exposure of movies that are underexposed. Because it has a low barrier to entry theoretically and can reliably give perceptually pleasant outcomes without adding artifacts, it is ideal for a wide variety of practical applications. This is because it is useful for a wide variety of practical applications. We demonstrate the usefulness of the method by displaying improved films of good quality that were made from a variety of different sorts of underexposed videos. A novel approach to the enhancement and editing of video is presented by our method. Rather than relying on a single heuristic transform function, it makes use of human visual perception to adaptively customize the overall visual appearance of the finished product. We believe that our work has the potential to make a big influence in the field of perception-aware video editing and the applications that are related to it, and that it is an important contribution to the community that works on improving videos. The challenge of video enhancement can be formulated as follows: given a video of low quality as the input, produce a video of high quality as the output, but only for specific uses. Whether it be in terms of the video objective clarity or its more subjective qualities, we are interested in learning how we might improve its overall quality.Keywords
Visual Appearance, Video Enhancement, Machine Learning.References
- M. Bhende and V. Saravanan, Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection, BioMed Research International, Vol. 2022, pp. 1-9, 2022.
- K.C. Chan and C.C. Loy, BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment, Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5972-5981, 2022.
- C. Nithya and V. Saravanan, A Study of Machine Learning Techniques in Data Mining, International Journal of Scientific Research, Vol. 1, 31-38, 2018.
- D. Wu and R. Wang, Edge-Cloud Collaboration Enabled Video Service Enhancement: A Hybrid Human-Artificial Intelligence Scheme, IEEE Transactions on Multimedia, Vol. 23, pp. 2208-2221, 2021.
- S. Tulyakov, J. Erbach and D. Scaramuzza, Time Lens: Event-Based Video Frame Interpolation, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16155-16164, 2021.
- K. Asha and M. Rizvana, Human Vision System Region of Interest Based Video Coding, Compusoft, Vol. 2, No. 5, pp. 127-138, 2013.
- I. Dave, M.N. Rizve and M. Shah, TCLR: Temporal Contrastive Learning for Video Representation, Computer Vision and Image Understanding, Vol. 219, pp. 103406-103418, 2022.
- P. Karthika and P. Vidhya Saraswathi, IoT using Machine Learning Security Enhancement in Video Steganography Allocation for Raspberry Pi, Journal of Ambient Intelligence and Humanized Computing, Vol. 12, No. 6, pp. 5835-5844, 2021.
- S. Niklaus, Revisiting Adaptive Convolutions for Video Frame Interpolation, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1099-1109, 2021.
- S. Bertoni and A. Facoetti, Action Video Games Enhance Attentional Control and Phonological Decoding in Children with Developmental Dyslexia, Brain Sciences, Vol. 11, No. 2, pp. 171-181, 2021.
- S. Yang, Y. Shan and W. Liu, Crossover Learning for Fast Online Video Instance Segmentation, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8043-8052, 2011.
- S. Selvi and V. Saravanan,. Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network, ICTACT Journal on Soft Computing, Vol. 11, No. 4, pp. 2438-2443, 2021.