Abstract
With rising challenges and depleting resources, many automation solutions have been developed in agriculture. Integration of Internet-of-Agro-Things (IoAT) and Artificial Intelligence (AI) helped gain better yields while maximizing utilization of minimal resources. Weed management being a task affecting quality and yield of crop attracted attention of automation. However, due to the diverse nature of agriculture, same crop from various geographical locations in different growth stages exhibit different features. Additionally, unknown weeds might also exist in the farm rendering feature based supervised CNN solutions not suitable for weed classification. The current paper presents a weed management Agriculture Cyber-Physical System (A-CPS) called WeedOut with a novel methodology enabling it to work in feature variant environments. WeedOut uses a Semi-Supervised methodology that classifies crops by their shapes and labels them as primary crop and weed crop with minimal inputs from farmer. An autonomous weed sprayer uses outputted labeled images to spray herbicide at weed locations and save primary crop.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mohanty, S.P.: Internet-of-agro-things (IoAT) makes smart agriculture. IEEE Consum. Electron. Mag. 10(4), 4–5 (2021)
Mitra, A., et al.: Everything you wanted to know about smart agriculture (2022)
Ekwealor, K.U., Echereme, C.B., Ofobeze, T.N., Okereke, C.N.: Economic importance of weeds: a review. Asian J. Plant Sci. 3, 1–11 (2019)
Kudsk, P., Streibig, J.C.: Herbicides-a two-edged sword. Weed Res. 43(2), 90–102 (2003)
Slaughter, D.C., Giles, D.K., Downey, D.: Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61(1), 63–78 (2008)
Louargant, M., et al.: Unsupervised classification algorithm for early weed detection in row-crops by combining spatial and spectral information. Remote Sens. 10(5), 761 2018
Ota, K., Kasahara, J.L.Y., Yamashita, A., Asama, H.: Weed and crop detection by combining crop row detection and k-means clustering in weed infested agricultural fields. In: Proceedings IEEE/SICE International Symposium on System Integration (SII), pp. 985–990 (2022)
Bah, M.D., Hafiane, A., Canals, R.: Weeds detection in UAV imagery using SLIC and the hough transform. In: Proceedings Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6 (2017)
Rani, K.A.A., Supriya, P., Sarath, T.V.: Computer vision based segregation of carrot and curry leaf plants with weed identification in carrot field. In: Proceedings International Conference on Computing Methodologies and Communication (ICCMC), pp. 185–188 (2017)
Tejeda, A.J.I., Castro, R.C.: Algorithm of weed detection in crops by computational vision. In: Proceedings International Conference on Electronics, Communications and Computers (CONIELECOMP), pp. 124–128 (2019)
Aravind, R., Daman, M., Kariyappa, B.S.: Design and development of automatic weed detection and smart herbicide sprayer robot. In: Proceedings of IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 257–261 (2045)
Siddiqi, M.H., Ahmad, I., Sulaiman, S.B.: Weed recognition based on erosion and dilation segmentation algorithm. In: Proceedings of International Conference on Education Technology and Computer, pp. 224–228 (2009)
Persson, M., Åstrand, B.: Classification of crops and weeds extracted by active shape models. Biosys. Eng. 100(4), 484–497 (2008)
Mitra, A., Singhal, A., Mohanty, S.P., Kougianos, E., Ray, C.: eCrop: a novel framework for automatic crop damage estimation in smart agriculture. SN Comput. Sci. 3(4), 319 (2022)
Mitra, A., Mohanty, S.P., Kougianos, E.: aGROdet: a novel framework for plant disease detection and leaf damage estimation. In: Proceedings of the 5th IFIP International Internet of Things Conference (IFIP-IoT), pp. 3–22 (2022)
Dabhi, R., Makwana, D.: Crop and weed detection data with bounding boxes (2020). https://www.kaggle.com/datasets/ravirajsinh45/crop-and-weed-detection-data-with-bounding-boxes. Accessed 20 Jan 2023
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 IFIP International Federation for Information Processing
About this paper
Cite this paper
Kethineni, K.K., Mitra, A., Mohanty, S.P., Kougianos, E. (2024). WeedOut: An Autonomous Weed Sprayer in Smart Agriculture Framework Using Semi-Supervised Non-CNN Annotation. In: Puthal, D., Mohanty, S., Choi, BY. (eds) Internet of Things. Advances in Information and Communication Technology. IFIPIoT 2023. IFIP Advances in Information and Communication Technology, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-031-45878-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-031-45878-1_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45877-4
Online ISBN: 978-3-031-45878-1
eBook Packages: Computer ScienceComputer Science (R0)
