International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

E-Marketplace Solution for Coconut that Matches Crop Supply and Demand in Sri Lanka

Abstract

This research paper presents an integrated emarketplace solution for the coconut industry, aiming to match crop supply efficiently. The system combines a coconut quality grading system using image processing, registration of farmers and collectors, algorithmic matching of buyers and sellers, supply visualization on a map, vehicle routing optimization, and a machine learning-based pricing and trend analysis dashboard. By integrating these functionalities, the solution enhances efficiency, transparency, and profitability in the coconut industry. This research contributes to advancing the industry in Sri Lanka by providing a comprehensive platform for seamless transactions, optimized transportation, and data-driven decision making. The proposed e-marketplace offers a holistic approach to connect stakeholders, streamline operations, and enable informed decision-making for sustainable growth in the coconut sector.

Country : Sri Lanka

1 Jeewakaratne S.S.U.D.S.2 Perera A.A.R.T.3 De Silva U.D.K.4 Perera S.A.A.5 Buddika Harshanath6 Samantha Rajapaksha

  1. Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  5. Department of Computer Science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  6. Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 11, November 2023 pp. 546-555

doi.org/10.47001/IRJIET/2023.711072

Full Paper
Download

References

  1. D. N. et al., “Coconut Disease Prediction System Using Image Processing and Deep Learning Techniques,” in Proc. 4th International Conference on Image Processing, Applications and Systems, IPAS 2020, 2020, pp. 212–217.
  2. T. N. N. et al., “CCIP: Proceedings: August 12-13, 2016, Sri Jayachamarajendra College of Engineering, JSS TI Campus, Manasagangothri, Mysuru 570006,” in Proceedings of the 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), 2016.
  3. S. K. B. et al., “Identification, classification grading of fruits using machine learning computer intelligence: a review,” 2020.
  4. B. T. W. P. et al., “The use of computer vision to estimate tree diameter and circumference in homogeneous and production forests using a noncontact method,” vol. 17, no. 1, pp. 32–38, 2021.
  5. V. Sathana, “Problems and challenges associated with value addition: with special reference to coconut-based productions in Jaffna District,” vol. 6, no. 0, p. 24, 2018.
  6. D. C. K. Gomathy, “A Study on Ecommerce Agriculture,” vol. 9, no. 10, pp. 1486–1488, 2021.
  7. P. Stodola, P. Otrisal, and K. Hasilova, “Adaptive Ant Colony Optimization with node clustering applied to the Travelling Salesman Problem,” in Proceedings of the Swarm and Evolutionary Computation, 2022, p. 101056.
  8. A.M. A. et al., “A novel Clustering based Genetic Algorithm for route optimization,” Engineering Science and Technology, an International Journal, vol. 19, no. 4, pp. 2022–2034, 2016.
  9. C. M. et al., “Connectivity-based optimization of vehicle route and speed for improved fuel economy,” in Proceedings of the Transportation Research Part C: Emerging Technologies, vol. 91, 2018, pp. 353–368.
  10. X. Huang and H. Peng, “Eco-Routing based on a Data Driven Fuel Consumption Model,” in Proceedings of the conference (or journal) where the paper was published, n.d.
  11. R. Chen and C. Gotsman, “Efficient fastest-path computations for road maps,” (Journal Name), vol. 7, no. 2, pp. 267–281, 2021.
  12. N. Udumulla, “Forecasting Export Prices of Sri Lankan Coconut Products Using Multivariate Time Series,” 2018.
  13. G. A. S. M. Padmasiri, “Coconut Price Prediction in Sri Lanka Using Supervised Machine Learning Approach (LSTM),” 2021.
  14. E. Pathiraja, G. R. Griffith, and R. Faggian, “The Sri Lankan Coconut Industry: Current Status and Future Prospects in a Changing Climate,” 2015.
  15. M. T. N. F. et al., “Economic Value of Climate Variability Impacts on Coconut Production in Sri Lanka,” 2007.
  16. I.Onder, “Forecasting tourism demand with Google trends: Accuracy comparison of countries versus cities,” 2017.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies