Mobile-Based AI Platform Integrating Image Analysis and Chatbot Technologies for Rice Variety and Weed Classification in Precision Agriculture

Wongpanya S. Nuankaew, Saweewan Kuisonjai, Raksita Keawruangrit, Pratya Nuankaew
Pages 2636-2650
Abstract Views 611
PDF Downloads 714
Crossref Citations -
DOI https://doi.org/10.47738/jads.v6i4.918

Abstract


This work presents the development of an intelligent chatbot system capable of identifying rice plants and weeds from aerial photographs captured by smartphones, thereby enhancing precision agriculture. The study involves creating an AI model that utilizes image processing and deep learning techniques. Users can access the model through a LINE chatbot, and the study will also assess users' satisfaction with the model. Researchers gathered 12,000 pictures of rice fields in Phayao Province, Thailand, to train a modified InceptionV3 model using transfer learning. The dataset included images of rice plants and various types of weeds. The model was trained using image data collected under natural lighting and augmented to improve generalization. It achieved training, validation, and testing accuracies of 98.79%, 96.08%, and 97.83%, respectively. When deployed through a LINE Chatbot, it analyzed user-submitted images to estimate rice-to-weed ratios, yielding 73.33% average accuracy with consistent rice detection. Thirty individuals who used the system reported that it functioned well, was user-friendly, and provided significant benefits for farming in real-world applications. These results suggest that the system could leverage easily accessible AI tools to enhance farming efficiency, reduce costs, and positively impact the environment.


Keywords


Aerial Image Analytics; Chatbot System; Plant Classification; Precision Agriculture; Rice Recognition; Weed Recognition

Full Text:

PDF

References


E. Elango, A. Hanees, B. Shanmuganathan, and M. I. Kareem Basha, “Precision Agriculture: A Novel Approach on AI-Driven Farming,” in Intelligent Robots and Drones for Precision Agriculture, S. Balasubramanian, G. Natarajan, and P. R. Chelliah, Eds., Cham: Springer Nature Switzerland, 2024, pp. 119–137. doi: 10.1007/978-3-031-51195-0_7.

F. J. Pierce and P. Nowak, “Aspects of Precision Agriculture,” in Advances in Agronomy, vol. 67, D. L. Sparks, Ed., Academic Press, 1999, pp. 1–85. doi: 10.1016/S0065-2113(08)60513-1.

N. Zhang, M. Wang, and N. Wang, “Precision agriculture—a worldwide overview,” Comput. Electron. Agric., vol. 36, no. 2, pp. 113–132, Nov. 2002, doi: 10.1016/S0168-1699(02)00096-0.

C. Jamroen, P. Komkum, C. Fongkerd, and W. Krongpha, “An Intelligent Irrigation Scheduling System Using Low-Cost Wireless Sensor Network Toward Sustainable and Precision Agriculture,” IEEE Access, vol. 8, pp. 172756–172769, 2020, doi: 10.1109/ACCESS.2020.3025590.

M. Sanyaolu and A. Sadowski, “The Role of Precision Agriculture Technologies in Enhancing Sustainable Agriculture,” Sustainability, vol. 16, no. 15, Art. no. 15, Jan. 2024, doi: 10.3390/su16156668.

A. Upadhyay et al., “Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review,” Comput. Electron. Agric., vol. 225, p. 109363, Oct. 2024, doi: 10.1016/j.compag.2024.109363.

M. Weis, C. Gutjahr, V. Rueda Ayala, R. Gerhards, C. Ritter, and F. Schölderle, “Precision farming for weed management: techniques,” Gesunde Pflanz., vol. 60, no. 4, pp. 171–181, Dec. 2008, doi: 10.1007/s10343-008-0195-1.

R. K. Verma and K. Kishor, “Image Processing Applications in Agriculture With the Help of AI,” in Infrastructure Possibilities and Human-Centered Approaches With Industry 5.0, IGI Global Scientific Publishing, 2024, pp. 162–181. doi: 10.4018/979-8-3693-0782-3.ch010.

W. S. Nuankaew, S. Kuisonjai, R. Keawruangrit, P. Nasa-Ngium, and P. Nuankaew, “Harnessing AI for Agriculture: Aerial Imagery Analysis for Rice and Weed Classification in Paddy Fields Using Deep Learning,” in 2025 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Jan. 2025, pp. 731–736. doi: 10.1109/ECTIDAMTNCON64748.2025.10962031.

J. Jayanthi and K. A. Kumar, “Transformative Impact of AI-Driven Computer Vision in Agriculture,” in Artificial Intelligence Techniques in Smart Agriculture, S. S. Chouhan, A. Saxena, U. P. Singh, and S. Jain, Eds., Singapore: Springer Nature, 2024, pp. 129–150. doi: 10.1007/978-981-97-5878-4_9.

V. G. Dhanya et al., “Deep learning based computer vision approaches for smart agricultural applications,” Artif. Intell. Agric., vol. 6, pp. 211–229, Jan. 2022, doi: 10.1016/j.aiia.2022.09.007.

N. V, S. G. A, G. S, K. M, M. A, and T. S, “AgriBot : An Integrated Chatbot Platform for Precision Agriculture and Farmer Support using Deep Learning Techniques,” in 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Oct. 2024, pp. 1–6. doi: 10.1109/ICPECTS62210.2024.10780432.

T. M. Geethajali, P. B, P. K M, P. G. C M, and Priyanka, “Agroinsights Chatbot: Ai-Driven Precision Farming For Optimal Yields, Crop Selection, And Disease-Free Harvests,” in 2024 IEEE North Karnataka Subsection Flagship International Conference (NKCon), Sep. 2024, pp. 1–8. doi: 10.1109/NKCon62728.2024.10774802.

S. Coulibaly, B. Kamsu-Foguem, D. Kamissoko, and D. Traore, “Deep learning for precision agriculture: A bibliometric analysis,” Intell. Syst. Appl., vol. 16, p. 200102, Nov. 2022, doi: 10.1016/j.iswa.2022.200102.

T. Saranya, C. Deisy, S. Sridevi, and K. S. M. Anbananthen, “A comparative study of deep learning and Internet of Things for precision agriculture,” Eng. Appl. Artif. Intell., vol. 122, p. 106034, Jun. 2023, doi: 10.1016/j.engappai.2023.106034.

G. Bisetsa Jururyishya, F. Nzanywayingoma, R. Musabe, J. Claude Habimana, and C. Abingabiye, “Predicting Health Status of Maize Crops by Integrating IoT Technology and Inception-v3 Convolutional Neural Network in Precision Agriculture,” in Proceedings of Ninth International Congress on Information and Communication Technology, X.-S. Yang, S. Sherratt, N. Dey, and A. Joshi, Eds., Singapore: Springer Nature, 2024, pp. 141–152. doi: 10.1007/978-981-97-3305-7_11.

W. Wu, L. Huo, G. Yang, X. Liu, and H. Li, “Research into the Application of ResNet in Soil: A Review,” Agriculture, vol. 15, no. 6, Art. no. 6, Jan. 2025, doi: 10.3390/agriculture15060661.

S. Saleki and J. Tahmoresnezhad, “Agry: a comprehensive framework for plant diseases classification via pretrained EfficientNet and convolutional neural networks for precision agriculture,” Multimed. Tools Appl., vol. 83, no. 24, pp. 64813–64851, Jul. 2024, doi: 10.1007/s11042-023-17952-9.




DOI: https://doi.org/10.47738/jads.v6i4.918

Refbacks

  • There are currently no refbacks.


Barcode

Journal of Applied Data Sciences

ISSN:2723-6471 (Online)
Publisher:Bright Publisher
Website:http://bright-journal.org/JADS
Email:taqwa@amikompurwokerto.ac.id (principal contact)
  support@bright-journal.org (technical issues)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0