https://doi.org/10.4081/jae.2025.1741
Leveraging deep semantic segmentation for assisted weed detection
Published: 18 February 2025
Abstract Views: 42
PDF_early view: 15
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
In agriculture, it is crucial to identify and control weeds as these plant species pose a significant threat to the growth and development of crops by competing for vital resources such as nutrients, water, and light. A promising solution to this problem is adopting smart weed control systems (SWCS) that significantly reduce the use of harmful chemical products. Furthermore, SWCS leads to reduced production costs and a more sustainable and eco-friendly approach to farming. However, implementing SWCS in natural fields can be challenging, mainly due to difficulties in accurately localizing plants. To address this issue, a visual identification system can be employed to label plants from images using a process known as semantic segmentation. In this work, we have implemented, validated, and compared three deep learning approaches, including Mask Region-based Convolutional Neural Network (Mask R-CNN), Mask R-CNN enhanced with an Atrous Spatial Pyramid Pooling module (Mask R-CNN-ASPP), and a proposed model named Residual U-Net architecture, for the semantic pixel segmentation of high densities of both crops (Zea mays) and weeds (including narrow-leaf weeds and broad-leaf weeds). Data augmentation and transfer learning have also been implemented. The performance of the models was evaluated with the well-known metrics Precision, Recall, Dice similarity coefficient (DSC), and mean Intersection-Over-Union (mIoU). As a result of the analysis, the DSC and mIoU of Mask R-CNN-ASPP based models were up to 10.63% and 10.54% superior to that of the Mask R-CNN based models. Nonetheless, the proposed Residual U-Net architecture outperformed Mask R-CNN-ASPP based networks in all the metrics, reaching a DSC of 92.98% and mIoU of 87.12%. Thus, we have concluded that the proposed Residual U-Net-like architecture is the best alternative for the semantic segmentation task in images with high plant density. Our research addresses the challenge of weed identification and control in agriculture, helping farmers produce crops more efficiently while minimizing environmental impact.
Arai, K., Barakbah, A.R. 2007. Hierarchical k-means: an algorithm for centroids initialization for k-means. Rep. Fac. Sci. Eng. 36:22-31.
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L. 2017. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE T. Pattern Anal. 40:834-848.
DOI: https://doi.org/10.1109/TPAMI.2017.2699184
Dentika, P., Ozier-Lafontaine, H., Penet, L. 2021. Weeds as pathogens hosts and disease risk for crops in the wake of a reduced use of herbicides: Evidence from yam (Dioscorea alata) fields and Colletotrichum pathogens in the tropics. J. Fungi 7:283.
DOI: https://doi.org/10.3390/jof7040283
Dutta, A., Zisserman, A. 2019. The VIA annotation software for images, audio and video. MM ’19, Proc. 27th ACM Int. Conf. on Multimedia, New York, pp. 2276-2279.
DOI: https://doi.org/10.1145/3343031.3350535
Dyrmann, M., Mortensen, A.K., Midtiby, H.S., Jørgensen, R.N. 2016. Pixel-wise classification of weeds and crops in images by using a fully convolutional neural network. In: CIGR-AgEng conference, Aarhus. Available from: https://conferences.au.dk/uploads/tx_powermail/cigr2016paper_semanticsegmentation.pdf
Fawakherji, M., Youssef, A., Bloisi, D.D., Pretto, A., Nardi, D. 2020. Crop and weed classification using pixel-wise segmentation on ground and aerial images. Int. J. Robot. Comput. 2:39-57.
DOI: https://doi.org/10.35708/RC1869-126258
Garibaldi-Márquez, F., Flores, G., Mercado-Ravell, D.A., Ramírez-Pedraza, A., Valentín-Coronado, L.M. 2022. Weed classification from natural corn field-multi-plant images based on shallow and deep learning. Sensors (Basel) 22:3021.
DOI: https://doi.org/10.3390/s22083021
He, K., Gkioxari, G., Dollár, P., Girshick, R. 2017. Mask R-CNN. Proc. 2017 IEEE Int. Conf. on Computer Vision (ICCV), Venice; pp. 2980-2988.
DOI: https://doi.org/10.1109/ICCV.2017.322
He, K., Zhang, X., Ren, S., Sun, J. 2016. Deep residual learning for image recognition. Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas; pp. 770-778.
DOI: https://doi.org/10.1109/CVPR.2016.90
Jadhav, S.B., Udupi, V.R., Patil, S. 2021. Identification of plant diseases using convolutional neural networks. Int. J. Inf. Tecnol. 13:2461-2470.
DOI: https://doi.org/10.1007/s41870-020-00437-5
Kamath, R., Balachandra, M., Vardhan, A., Maheshwari, U. 2022. Classification of paddy crop and weeds using semantic segmentation. Cogent Engin.9:2018791.
DOI: https://doi.org/10.1080/23311916.2021.2018791
Khan, A., Talha, I., Umraiz, M., Ibna Mannan, Z., Kim, H. 2020. Ced-net: Crops and weeds segmentation for smart farming using a small cascaded encoder-decoder architecture. Electronics (Basel) 9:1602.
DOI: https://doi.org/10.3390/electronics9101602
Kolhar, S., Jayant, J. 2021. Convolutional neural network based encoder-decoder architectures for semantic segmentation of plants. Ecol Inform, 64:101373.
DOI: https://doi.org/10.1016/j.ecoinf.2021.101373
Krizhevsky, A., Sutskever, I., Hinton, G. 2012. ImageNet classification with deep convolutional neural networks. In: F. Pereira, C.J.C. Burges, L. Bottou and K. Weinberger (eds.), Adv. in Neural Inform. Process. Syst. 25. Curran Associates, Inc. Lake Tahoe.
Long, J., Shelhamer, E., Darrell, T. 2015. Fully convolutional networks for semantic segmentation. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston; pp. 3431-3440.
DOI: https://doi.org/10.1109/CVPR.2015.7298965
Ma, X., Deng, X., Qi, L., Jiang, Y., Li, H., Wang, Y., Xing, X. 2019. Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields. PLoS One 14:e0215676.
DOI: https://doi.org/10.1371/journal.pone.0215676
Monteiro, A,. Santos, S. 2022. Sustainable approach to weed management: The role of precision weed management. Agronomy (Basel) 12:118.
DOI: https://doi.org/10.3390/agronomy12010118
Montes de Oca, A., Arreola, L., Flores, A., Sánchez, J., Flores, G. 2018. Low-cost multispectral imaging system for crop monitoring. Proc. Int. Conf. on Unmanned Aircraft Systems (ICUAS), Dallas; pp. 443-451.
DOI: https://doi.org/10.1109/ICUAS.2018.8453426
Montes de Oca, A., Flores, G. 2021a. The AgriQ: A low-cost unmanned aerial system for precision agriculture. Expert Syst. Appl. 182:115–163.
DOI: https://doi.org/10.1016/j.eswa.2021.115163
Montes de Oca, A., Flores, G. 2021b. A UAS equipped with a thermal imaging system with temperature calibration for crop water stress index computation. Proc. Int. Conf. on Unmanned Aircraft Systems (ICUAS), Athens; pp. 714-720.
DOI: https://doi.org/10.1109/ICUAS51884.2021.9476863
Nedeljković, D., Knežević, S., Božić, D., Vrbnićanin, S. 2021. Critical time for weed removal in corn as influenced by planting pattern and pre-herbicides. Agriculture (Basel) 11:587.
DOI: https://doi.org/10.3390/agriculture11070587
Nikolić, N., Rizzo, D., Marraccini, E., Ayerdi Gotor, A., Mattivi, P., Saulet, P., Persichetti, A., Masin, R. 2021. Site- and time-specific early weed control is able to reduce herbicide use in maize- a case study. Ital. J. Agron. 16:1780.
DOI: https://doi.org/10.4081/ija.2021.1780
Peng, H., Li, Z., Zhou, Z., Shao, Y. 2022. Weed detection in paddy field using an improved RetinaNet network. Comput. Electron. Agr. 199:107179.
DOI: https://doi.org/10.1016/j.compag.2022.107179
Picon, A., San-Emeterio, M., Bereciartua-Perez, A., Klukas, C., Eggers, T., ad Navarra- Mestre, R. 2022. Deep learning-based segmentation of multiple species of weeds and corn crop using synthetic and real image datasets. Comput. Electron. Agr. 194:106719.
DOI: https://doi.org/10.1016/j.compag.2022.106719
Quan, L., Wu, B., Mao, S., Yang, C., Li, H. 2021. An instance segmentation-based method to obtain the leaf age and plant centre of weeds in complex field environments. Sensors (basel) 21:3389.
DOI: https://doi.org/10.3390/s21103389
Ren, X. Malik, J. 2003. Learning a classification model for segmentation. Proc. 9th IEEE Int. Conf. on Computer Vision, Nice; pp. 10-17.
DOI: https://doi.org/10.1109/ICCV.2003.1238308
Ronneberger, O., Fischer, P., Brox, T. 2015. U-net: Convolutional networks for biomedical image segmentation. In N. Navab, J. Hornegger, W. Wells, and A. Frangi (eds.), Medical image computing and computer-assisted intervention Vol. 9351. Lecture Notes in Computer Science. Cham, Springer. pp 234-241.
DOI: https://doi.org/10.1007/978-3-319-24574-4_28
Shi, J., Malik, J. 2000. Normalized cuts and image segmentation. IEEE T. Pattern Anal. 22:888-905.
DOI: https://doi.org/10.1109/34.868688
Simonyan, K., Zisserman, A. 2015. Very deep convolutional networks for large-scale image recognition. Proc. 3rd Int. Conf. on Learning Representations, San Diego.
Taha, M.F., Abdalla, A., ElMasry, G., Gouda, M., Zhou, L., Zhao, N., et al. 2022. Using deep convolutional neural network for image-based diagnosis of nutrient deficiencies in plants grown in aquaponics. Chemosensors (Basel) 10:45.
DOI: https://doi.org/10.3390/chemosensors10020045
Tang, J.L., Chen, X.Q., Miao, R.H., Wang, D. 2016. Weed detection using image precision under different illumination for site-specific areas spraying. Comput. Electron. Agr. 122:103-111.
DOI: https://doi.org/10.1016/j.compag.2015.12.016
Zenkl, R., Timofte, R., Kirchgessner, N., Roth, L., Hund, A., Van Gool, L., et al. 2022. Outdoor plant segmentation with deep learning for high-throughput field phenotyping on a diverse wheat dataset. Front. Plant Sci. 12:774068.
DOI: https://doi.org/10.3389/fpls.2021.774068
Zhang, H., Peng, Q. 2022. Pso and k-means-based semantic segmentation toward agricultural products. Future Gener. Comp. Sy. 126:82–87.
DOI: https://doi.org/10.1016/j.future.2021.06.059
How to Cite
Garibaldi-Márquez, F., Flores, G. and Valentín-Coronado, L. M. (2025) “Leveraging deep semantic segmentation for assisted weed detection”, Journal of Agricultural Engineering. doi: 10.4081/jae.2025.1741.
Copyright (c) 2025 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Lucia Recchia, Daniele Sarri, Marco Rimediotti, Paolo Boncinelli, Enrico Cini, Marco Vieri, Towards the environmental sustainability assessment for the viticulture , Journal of Agricultural Engineering: Vol. 49 No. 1 (2018)
- Ugo Lazzaro, Caterina Mazzitelli, Benedetto Sica, Paola Di Fiore, Nunzio Romano, Paolo Nasta, On evaluating the hypothesis of shape similarity between soil particle-size distribution and water retention function , Journal of Agricultural Engineering: Vol. 54 No. 4 (2023)
- Giuseppe Ruggiero, Giuseppe Verdiani, Stefano Dal Sasso, Evaluation of carrying capacity and territorial environmental sustainability , Journal of Agricultural Engineering: Vol. 43 No. 2 (2012)
- Shinar Ospanova, Mubarak Aduov, Sultan Kapov, Alexander Orlyansky, Kadirbek Volodya, The results of experimental research of a rotor seed-metering unit for sowing non-free-flowing seeds , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Andrea Petroselli, Dario Romerio, Piero Santelli, Roberto Mariotti, Silvano Di Giacinti, Luca Casini, Carmine Testa, Assessing sprinkler systems performance with a novel experimental benchmark , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Salvatore Praticò, Raimondo Tripodi, Viviana Tirella, Salvatore Di Fazio, Francesco Barreca, Giuseppe Modica, Proposal of an integrated 3D architectural survey method for application in historic agri-food building analysis and representation , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Xin Yu, Ling Zhao, Zongbin Liu, Yiqing Zhang, Distinguishing tea stalks of Wuyuan green tea using hyperspectral imaging analysis and convolutional neural network , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Giorgia Cocolo, Silvia Curnis, Maibritt Hjorth, Giorgio Provolo, Effect of different technologies and animal manures on solid-liquid separation efficiencies , Journal of Agricultural Engineering: Vol. 43 No. 2 (2012)
- Alberto Cadei, Omar Mologni, Luca Marchi, Francesco Sforza, Dominik Röser, Raffaele Cavalli, Stefano Grigolato, Energy efficiency of a hybrid cable yarding system: A case study in the North-Eastern Italian Alps under real working conditions , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Simona M.C. Porto, Giulia Castagnolo, Francesca Valenti, Giovanni Cascone, Kernel density estimation analyses based on a low power-global positioning system for monitoring environmental issues of grazing cattle , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
<< < 14 15 16 17 18 19 20 21 22 23 > >>
You may also start an advanced similarity search for this article.
