Naveen Kumar V.M.
K.V. Rahul
Gopika T.M
Simi Chandran
Sujith D.K
- Student Department of Electrical and Electronics Engineering, College of Engineering Trikaripur, Kasaragod Kerala India
- Student Department of Electrical and Electronics Engineering, College of Engineering Trikaripur, Kasaragod Kerala India
- Student Department of Electrical and Electronics Engineering, College of Engineering Trikaripur, Kasaragod Kerala India
- Student Department of Electrical and Electronics Engineering, College of Engineering Trikaripur, Kasaragod Kerala India
- Assistant Professor Department of Electrical and Electronics Engineering, College of Engineering Trikaripur, Kasaragod Kerala India
Abstract
Agriculture serves as the foundation of global civilization, particularly in nations like India, where it accounts for roughly 70% of the GDP, underscoring its pivotal role in economic stability. Despite its significance, traditional agricultural practices have often overlooked critical elements such as disease identification and precise pesticide application, focusing primarily on conventional methods like harvesting and seedling techniques. To address these gaps and usher in a new era of efficiency and sustainability, there is a growing imperative for modernization, particularly in disease detection and precision agriculture, necessitating the integration of cutting-edge technologies. The emergence of advanced image processing technology represents a transformative breakthrough, exemplified by solutions like AGROBOT, which enable the precise detection of leaf diseases and targeted pesticide application, thus enhancing agricultural productivity while mitigating environmental concerns such as water contamination and soil degradation. By prioritizing disease management and precision agriculture, we can cultivate sustainable farming practices that not only ensure food security but also amplify overall agricultural output. Embracing technological innovations is essential to confront the evolving challenges facing the agricultural sector, from the need for increased efficiency to the imperative of minimizing environmental footprints and bolstering food production. Through the seamless integration of modern technology and pioneering solutions like Agrobot, we chart a course toward a future where agriculture is more resilient, sustainable, and equipped to meet the demands of a growing global population, ensuring food security for generations to come.
Keywords: Agriculture, modernization, disease detection, precision agriculture, bolstering food production
[This article belongs to Journal of Experimental & Applied Mechanics(joeam)]
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References
- Nitin Krishna V, Ragunath B, Kowshika Priya B, Sivaranjani M, Vasanthamani K “Smart Farm Assist Robot”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958, Volume-10 Issue-1, October 2020
- Young-Jae Ryoo, Ki-Nam Lee& Pil-Gong Choi(2012), “Intelligent Platform Design of Agricultural Robot Inspired by Farmer Assistance (AGRIFA)”, SCIS-ISIS 2012, Kobe, Japan
- Hari Mohan Rai, Deepak Gupta, Sandeep Mishra, Himanshu Sharma “Agri-Bot: IoT Based unmanned smart Vehicle for multiple agriculture operation”, 2021 International Conference on Simulation, Automation & Smart Manufacturing (SASM) GLA University, Mathura, India. August 20-21, 2021
- Bagyaveereswaran V, Ankita Ghorui, R. Anitha “Automation of Agricultural Tasks with Robotics- Agrobot”, 2019 Innovations in Power and Advanced Computing Technologies (i-PACT)
- Bhaskar S, Pradeep Kumar M, Avinash M N& Harshini S B,” Real Time Farmer Assistive Flower Harvesting Agricultural Robot”, 2021 6th International Conference for Convergence in Technology (I2CT) Pune, India. Apr 02-04, 2021.
- T Dharanika, Ruban Karthik S, Sabhariesh Vel S, Vyaas S& Yogeshwaran S,” Automatic Leaf Disease Identification and Fertilizer Agrobot”, 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)
- Smith A & Johnson B. (2020). “Advances in leaf disease detection using machine learning techniques”. Journal of Agricultural Science, 25(3), 123-135.
- Patel, S., Gupta, R., & Singh, M. (2019). “Deep learning-based approach for automated detection of leaf diseases”. International Journal of Computer Applications, 178(3), 10-18.
- Kumar, V., Sharma, S., & Singh, P. (2018). “Review on recent trends in image processing techniques for leaf disease detection”. International Journal of Computer Science and Information Technologies, 9(5), 210-215.
- Chen, Y., Li, X., & Wang, Z. (2017). “Leaf disease detection using convolutional neural networks”. Computers and Electronics in Agriculture, 139, 272-281.
- Das, S., Mishra, S., & Mohanty, S. (2016).” Leaf disease detection using digital image processing techniques:” A review. International Journal of Computer Applications, 152(7), 15-20.
Journal of Experimental & Applied Mechanics
| Volume | 15 |
| Issue | 01 |
| Received | May 20, 2024 |
| Accepted | June 15, 2024 |
| Published | June 18, 2024 |