SMARD: A Cost Effective Smart Agro Development Technology for Crops Disease Classification (2405.10543v1)
Abstract: Agriculture has a significant role in a country's economy. The "SMARD" project aims to strengthen the country's agricultural sector by giving farmers with the information and tools they need to solve common difficulties and increase productivity. The project provides farmers with information on crop care, seed selection, and disease management best practices, as well as access to tools for recognizing and treating crop diseases. Farmers can also contact the expert panel through text message, voice call, or video call to purchase fertilizer, seeds, and pesticides at low prices, as well as secure bank loans. The project's goal is to empower farmers and rural communities by providing them with the resources they need to increase crop yields. Additionally, the "SMARD" will not only help farmers and rural communities live better lives, but it will also have a good effect on the economy of the nation. Farmers are now able to recognize plant illnesses more quickly because of the application of machine learning techniques based on image processing categorization. Our experiments' results show that our system "SMARD" outperforms the cutting-edge web applications by attaining 97.3% classification accuracy and 96% F1-score in crop disease classification. Overall, our project is an important endeavor for the nation's agricultural sector because its main goal is to give farmers the information, resources, and tools they need to increase crop yields, improve economic outcomes, and improve livelihoods.
- S. N. Nobel, M. A. H. Wadud, A. Rahman, D. Kundu, A. A. Aishi, S. Sazzad, M. Rahman, M. A. Imran, O. F. Sifat, M. Sayduzzaman et al., “Categorization of dehydrated food through hybrid deep transfer learning techniques,” Statistics, Optimization & Information Computing, 2024.
- A. Rahman, C. Chakraborty, A. Anwar, M. Karim, M. Islam, D. Kundu, Z. Rahman, S. S. Band et al., “Sdn–iot empowered intelligent framework for industry 4.0 applications during covid-19 pandemic,” Cluster Computing, vol. 25, no. 4, pp. 2351–2368, 2022.
- A. Rahman, M. J. Islam, A. Montieri, M. K. Nasir, M. M. Reza, S. S. Band, A. Pescape, M. Hasan, M. Sookhak, and A. Mosavi, “Smartblock-sdn: An optimized blockchain-sdn framework for resource management in iot,” IEEE Access, vol. 9, pp. 28 361–28 376, 2021.
- Planetnatural, “Plant diseases: Identification — pest problem solver,” https://www.planetnatural.com/pest-problem-solver/plant-disease/, 2014.
- Wikipedia contributors, “Lists of plant diseases the free encyclopedia,” 2022, [Online; accessed 20-February-2023].
- A. Rahman, T. Debnath, D. Kundu, M. S. I. Khan, A. A. Aishi, S. Sazzad, M. Sayduzzaman, and S. S. Band, “Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities,” AIMS Public Health, vol. 11, no. 1, pp. 58–109, 2024.
- S. A. Miah, A. K. M. Shahjahan, M. A. Hossain, and N. R. Sharma, “A survey of rice diseases in bangladesh,” Tropical Pest Management, vol. 31, no. 3, pp. 208–213, 1985.
- A. Rahman, J. Islam, D. Kundu, R. Karim, Z. Rahman, S. S. Band, M. Sookhak, P. Tiwari, and N. Kumar, “Impacts of blockchain in software-defined internet of things ecosystem with network function virtualization for smart applications: Present perspectives and future directions,” International Journal of Communication Systems, p. e5429, 2023.
- Plant Clinic, “Questions and answers/problems and solutions related to various crop diseases.” http://plantdiseaseclinic.com, 2019-03-20.
- Public Kathryn Fidler, “Farmers.gov: Resources for farmers and producers,” https://www.farmers.gov, 2023-01-13.
- Agriculture Information Service (AIS), “Agriculture information service (ais) government of the people’s republic of bangladesh,” http://www.ais.gov.bd/, 2023-03-02.
- A. I. Udoy, M. A. Rahaman, M. J. Islam, A. Rahman, Z. Ali, and G. Muhammad, “4sqr-code: A 4-state qr code generation model for increasing data storing capacity in the digital twin framework,” Journal of Advanced Research, 2023.
- Chowdappa, “Diseases of vegetable crops,” 5/22/14.
- A. Rahman, M. J. Islam, S. S. Band, G. Muhammad, K. Hasan, and P. Tiwari, “Towards a blockchain-sdn-based secure architecture for cloud computing in smart industrial iot,” Digital Communications and Networks, vol. 9, no. 2, pp. 411–421, 2023.
- Farming Bangladesh, “Farming future bangladesh,” https://www.farmingfuturebd.com, 2022-04-03.
- A. Rahman, K. Hasan, D. Kundu, M. J. Islam, T. Debnath, S. S. Band, and N. Kumar, “On the icn-iot with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives,” Future Generation Computer Systems, vol. 138, pp. 61–88, 2023.
- G. Jocher, “YOLOv5 by Ultralytics,” May 2020. [Online]. Available: https://github.com/ultralytics/yolov5
- M. S. I. Khan, A. Rahman, S. Islam, M. K. Nasir, S. S. Band, and A. Mosavi, “Iot and wsn based effluent treatment plant monitoring system,” EasyChair Preprint no. 5023, EasyChair, 2021.
- M. Jung, J. S. Song, A.-Y. Shin, B. Choi, S. Go, S.-Y. Kwon, J. Park, S. G. Park, and Y.-M. Kim, “Construction of deep learning-based disease detection model in plants,” Scientific Reports, vol. 13, no. 1, p. 7331, 2023.
- A. Rahman, M. S. Hossain, G. Muhammad, D. Kundu, T. Debnath, M. Rahman, M. S. I. Khan, P. Tiwari, and S. S. Band, “Federated learning-based ai approaches in smart healthcare: concepts, taxonomies, challenges and open issues,” Cluster computing, vol. 26, no. 4, pp. 2271–2311, 2023.