RANAIVOSON, Tojonirina and RASAMIMANANA, Hantanirina Rosiane and RASOANAIVO, Andriniaina Narindra and ANDRIANARIMANANA, Omer and ANDRIAMAMONJY, Alfred and RAZAFIMAHATRATRA, Dieudonné (2024): Weed detection in a Rice Crop through Image Processing and Classification Using Convolutional Neural Networks. Forthcoming in:
![]() |
PDF
MPRA_paper_123474.pdf Download (678kB) |
Abstract
Artificial Intelligence (AI) today occupies a central ranking, especially in a context where technological progress is omnipresent. Among the most influential tools, deep learning has established itself in both professional and academic domains. This article focuses on the effectiveness of convolutional neural networks for detecting weeds competing with rice. To achieve this, an extension of the pre-trained Inception_V3 model was used for image classification, while MobileNet was employed for image processing. This innovative approach, tested on a rice field where distinguishing between rice and weeds is challenging, represents a significant advancement in the AI field. However, the training of both models revealed limitations: Inception_V3 exhibited overfitting after the 10th iteration, while MobileNet showed high volatility and overfitting from the first iteration. Despite these challenges, Inception_V3 stood out for its superior accuracy.
Item Type: | MPRA Paper |
---|---|
Original Title: | Weed detection in a Rice Crop through Image Processing and Classification Using Convolutional Neural Networks |
English Title: | Weed detection in a Rice Crop through Image Processing and Classification Using Convolutional Neural Networks |
Language: | English |
Keywords: | Convolutional, Neural, Pre-trained, Detection |
Subjects: | Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture > Q16 - R&D ; Agricultural Technology ; Biofuels ; Agricultural Extension Services |
Item ID: | 123474 |
Depositing User: | Mr Tojonirina RANAIVOSON |
Date Deposited: | 11 Feb 2025 14:36 |
Last Modified: | 11 Feb 2025 14:36 |
References: | Abbas, T., Zahir, Z. A., Naveed, M., & Kremer, R. J. (2018). Limitations of existing weed control practices necessitate development of alternative techniques based on biological approaches. Advances in Agronomy, 147, 239-280. AgroTIC, C. (2018). Deep learning et Agriculture : comprendre le potentiel et les défis à relever. Basha, S. S., Dubey, S. R., Pulabaigari, V., & Mukherjee, S. (2020). Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocom- puting, 378, 112-119. Buhler, D. D. (2002). 50th Anniversary—Invited Article : Challenges and opportunities for inte- grated weed management. Weed Science, 50 (3), 273-280. dos Santos Ferreira, A., Freitas, D. M., da Silva, G. G., Pistori, H., & Folhes, M. T. (2017). Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture, 143, 314-324. Griffon, M. (1999). Développement durable et agriculture : la révolution doublement verte. Guo, Z., Goh, H. H., Li, X., Zhang, M., & Li, Y. (2023). WeedNet-R : a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion. Frontiers in Plant Science, 14. Khan, M. A., Afridi, R. A., Hashim, S., Khattak, A. M., Ahmad, Z., Wahid, F., & Chauhan, B. S. (2016). Integrated effect of allelochemicals and herbicides on weed suppression and soil microbial activity in wheat (Triticum aestivum L.) Crop Protection, 90, 34-39. Lantz, B. (2019). Machine Learning with R (S. Jain, Éd.). Packt publising Ltd. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture : A review. Sensors, 18 (8), 2674. O’Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv :1511.08458. Rosle, R., Che’Ya, N. N., Ang, Y., Rahmat, F., Wayayok, A., Berahim, Z., Fazlil Ilahi, W. F., Ismail, M. R., & Omar, M. H. (2021). Weed detection in rice fields using remote sensing technique : A review. Applied sciences, 11 (22), 10701. Ruby, U., & Yendapalli, V. (2020). Binary cross entropy with deep learning technique for image classification. Int. J. Adv. Trends Comput. Sci. Eng, 9 (10). Sa, I., Chen, Z., Popović, M., Khanna, R., Liebisch, F., Nieto, J., & Siegwart, R. (2017). weednet : Dense semantic weed classification using multispectral images and mav for smart farming. IEEE robotics and automation letters, 3 (1), 588-595. Subeesh, A., Bhole, S., Singh, K., Chandel, N., Rajwade, Y., Rao, K., Kumar, S., & Jat, D. (2022). Deep convolutional neural network models for weed detection in polyhouse grown bell peppers. Artificial Intelligence in Agriculture, 6, 47-54. https://doi.org/https://doi.org/ 10.1016/j.aiia.2022.01.002 Tuffery, S. (2023). Deep learning : From big data to artificial intelligence with R : Deep learning for natural language processing. Wu, Z., Chen, Y., Zhao, B., Kang, X., & Ding, Y. (2021). Review of weed detection methods based on computer vision. Sensors, 21 (11), 3647. Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2015). Loss functions for neural networks for image processing. arXiv preprint arXiv :1511.08861. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/123474 |