Various Weed Control Methods – An Analysis
AUTHOR(S)
Jeya Daisy I, Arjun M, Dakshin D S
DOI: https://doi.org/10.46647/ijetms.2023.v07i04.010
ABSTRACT
As the world's population continues to grow and food consumption rises, the agricultural industry will have a very difficult time fulfilling future demands. The growth of undesirable plants, or weeds, alongside crops is one of the most important problems in agriculture. Weeds increase agricultural costs and inhibit productivity. It necessitates using more water to irrigate the land. They somehow reduce the value of the food or raise the expense of cleaning. Some weeds (Cleome viscosa), which dairy cows consume, cause the milk to smell bad. Crops and weeds compete for nutrients, space, sunshine, and water. They also harbour viruses and insects that are harmful to agricultural plants. Additionally, they harm local animals and plants by destroying their natural habitats. As a result, these weeds must be removed in a timely manner to ensure the health of the crops. This report discussed some of the weed control approaches.
Page No: 47 - 51
References:
- Pusphavalli, M., & Chandraleka, R. (2016). Automatic Weed Removal System using Machine Vision. International Journal of advanced Research in Electronics and Communication Engineering (IJARECE), 5(3), 503-506
- Pramod, R., Sandesh, M. D., Jayaram, M. S., & Kalasagarreddi, K. (2014, December). Design and Development of Sustainable Weed Cutter. In 2014 3rd International Conference on Eco-friendly Computing and Communication Systems (pp. 287-292). IEEE.
- Osepchuk, J. M. (1980). B45 2-Stroke Petrol Grass Cutting Brush Cutter Machine.
- Mrs. Latha, A Poojith, B V Amarnath Reddy, G Vittal Kumar; “Image Processing In Agriculture”; IJIREEICE; 2014.
- [5] AnupVibhute, S K Bodhe; “Applications of Image Processing in Agriculture: A survey; International Journal of Computer Applications”; 2012
- Wu X, Aravecchia S, Lottes P, Stachniss C, Pradalier C. Robotic weed control using automated weed and crop classification. J Field Robotics. 2020;1–19
- Bechar, A., & Vigneault, C. (2017). Agricultural robots for field operations. Part 2: Operations and systems. Biosystems Engineering, 153, 110–128.
- Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., Lehnert, C., Perez, T., 2017. Robot for weed species plant-specific management. J. Field Robot. 34 (6), 1179– 1199.
- Haug, S., Michaels, A., Biber, P., Ostermann, J., 2014. Plant classification system for crop/ weed discrimination without segmentation. In: 2014 IEEE Winter Conference on Applications of Computer Vision, WACV, Steamboat Springs, CO, pp. 1142–1149.
- Utstumo, T., Berge, T., Gravdahl, J., 2015. Non-linear model predictive control for constrained robot navigation in row crops. In: Proceedings of the IEEE International Conference on Industrial Technology, Sevilla, Spain.
- Hall, J.C.; Eerd, L.L.V.; Miller, S.D.; Owen, M.D.K.; Prather, T.S.; Shaner, D.L.; Singh, M.; Vaughn, K.C.; Weller, S.C. Future Research Directions for Weed Science1. Weed Technol. 2000, 14, 647–658.
- Tillett, N.; Hague, T.; Grundy, A.; Dedousis, A. Mechanical within-row weed control for transplanted crops using computer vision. Biosyst. Eng. 2008, 99, 171–178
- Kunz, C.; Weber, J.; Gerhards, R. Benefits of precision farming technologies for mechanical weed control in soybean and sugar beet—Comparison of precision hoeing with conventional mechanical weed control.
- Shamkuwar, S. V., Baral, S. S., Budhe, V. K., Gupta, P., & Swarnkar, R. (2019). A critical study on weed control techniques. InternationalJournalofAdvancesinAgriculturalScienceandTechnology, 6(12), 1-22.
- Nedeljković, D., Knežević, S., Božić, D., & Vrbničanin, S. (2021). Critical Time for Weed Removal in corn as influenced by planting pattern and PRE herbicides. Agriculture, 11(7), 587.
How to Cite This Article:
Jeya Daisy I, Arjun M, Dakshin D S
. ijetms;7(3):47-51. DOI: 10.46647/ijetms.2023.v07i04.010