Abstract
Weeds are those unwanted plants that grow between cultivated crops, which reduce the purity of the crops. Crops are severely affected by weeds for their quality and yields. Farmers use the traditional method for weed removal that is time-consuming and also makes it difficult to identify the difference between weed and crop. This research proposes deep convolutional neural network based Inception V4 architecture approach for identifying weed density in soya bean crop fields using crop weed field image dataset (CFWID). This work uses RGB weed and crop images. It offers a data cleaning to eliminate background, and foreground vegetation using segmentation masked. Thereafter, the weed-density area is identified using vegetation segmentation, which is a major challenge in many of such research works. This approach is validated using the CFWID weed and crop dataset that consists of 1100 broadleaf, 2548 grass weed, and the remaining 736 weed images collected from soya bean crop fields and close-to-crop weeds. The proposed model achieves an accuracy of 98.2% using 4384 weed images. Therefore, the proposed approach has been generalized to different weed species in the soya bean crop without the need for extensive labelled data with the precision value of 97%, recall value as 99%, and F1 score as 98%.
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Data availability
Data has been available on Crop Weed Field Image Dataset (CFWID).
References
Ashraf T, Khan YN (2020) Weed density classification in rice crop using computer vision. Comput Electron Agric 175:105590. https://doi.org/10.1016/j.compag.2020.105590
Chebrolu N, Lottes P, Schaefer A, Winterhalter W, Burgard W, Stachniss C (2017) Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields. Int J Robot Res 36(10):1045–1052. https://doi.org/10.1177/0278364917720510
Elstone L, How KY, Brodie S, Ghazali MZ, Heath WP, Grieve B (2020) High speed crop and weed identification in lettuce fields for precision weeding. Sensors. https://doi.org/10.3390/s20020455
Emmert-Streib F, Moutari S, Dehmer M (2019) A comprehensive survey of error measures for evaluating binary decision making in data science. Wiley Online Libr. https://doi.org/10.1002/widm.1303
Farooq A, Jia X, Hu J, Zhou J (2019) Multi-resolution weed classification via convolutional neural network and superpixel based local binary pattern using remote sensing images. Mdpi Com. https://doi.org/10.3390/rs11141692
Gao J, French AP, Pound MP, He Y, Pridmore TP, Pieters JG (2020) Deep convolutional neural networks for image-based convolvulus sepium detection in sugar beet fields. Plant Methods. https://doi.org/10.1186/s13007-020-00570-z
Gharde Y, Singh PK (2021) Farmers’ knowledge level and constraints faced in the adoption of weed management technologies. Indian J Weed Sci 53(1):73–77. https://doi.org/10.5958/0974-8164.2021.00010.1
Gharde Y, Singh PK, Dubey RP, Gupta PK (2018) Assessment of yield and economic losses in agriculture due to weeds in India. Crop Prot 107:12–18. https://doi.org/10.1016/j.cropro.2018.01.007
Haug S, Ostermann J (2015) A crop/weed field image dataset for the evaluation of computer vision based precision agriculture tasks. Lect Notes Comput Sci Incl Subser Lect Notes Artif Intell Lect Notes Bioinform 8928:105–116. https://doi.org/10.1007/978-3-319-16220-1_8
Hu K, Wang Z, Coleman G, Bender A, Yao T, Zeng S, Song D, Schumann A, Walsh M (2021) Deep learning techniques for in-crop weed identification: A Review. https://arxiv.org/abs/2103.14872
Kaur P, Harnal S, Tiwari R, Alharithi FS, Almulihi AH, Noya ID, Goyal N (2021) A hybrid convolutional neural network model for diagnosis of COVID-19 using chest X-ray images. Int J Environ Res Public Health 18(22):12191
Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: a review. Sensors 18(8):2674. https://doi.org/10.3390/S18082674
Mishra AM, Gautam V (2021) Weed species identification in different crops using precision weed management: a review. CEUR Workshop Proc 2786:180–194
Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, Melgani F (2018) Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective. GigaScience 8(1):1–12. https://doi.org/10.1093/gigascience/giy153
Nagaraju M, Chawla P, Upadhyay S, and Tiwari R, (2021) Convolution network model based leaf disease detection using augmentation techniques. Expert Syst e12885
Nachiketh RV, Krishnan A, Krishnan KV, Haritha ZA, Sasinas A (2021) Southern pea/weed field image dataset for semantic segmentation and crop/weed classification using an encoder-decoder network. SSRN Electron J. https://doi.org/10.2139/ssrn.3781351
Olsen A, Konovalov DA, Philippa B, Ridd P, Wood JC, Johns J, Banks W, Girgenti B, Kenny O, Whinney J, Calvert B, Azghadi MR, White RD (2019) Deep weeds: a multiclass weed species image dataset for deep learning. Sci Rep 9(1):1–12. https://doi.org/10.1038/s41598-018-38343-3
Peng H, Li Q, Yuan LG (2020) Research on the automatic extraction method of web data objects based on deep learning. Intell Autom Soft Comput 26(3):609–616. https://doi.org/10.32604/iasc.2020.013939
Polder G, Blok PM, de Villiers HAC, van der Wolf JM, Kamp J (2019) Potato virus Y detection in seed potatoes using deep learning on hyperspectral images. Front Plant Sci 10:1–13. https://doi.org/10.3389/fpls.2019.00209
Rasti P, Ahmad A, Samiei S, Belin E, Rousseau D (2019) Supervised image classification by scattering transform with application toweed detection in culture crops of high density. Remote Sens. https://doi.org/10.3390/rs11030249
Sa I, Chen Z, Popovic M, Khanna R, Liebisch F, Nieto J, Siegwart R (2018) Weed net: dense semantic weed classification using multispectral images and mav for smart farming. IEEE Robot Autom Lett 3(1):588–595. https://doi.org/10.1109/LRA.2017.2774979
Schirrmann M, Landwehr N, Giebel A, Garz A, Karl-Heinz D (2021) Early detection of stripe rust in winter wheat using deep residual neural networks. Frontiersin Org 12:475
Shorewala S, Ashfaque A, Sidharth R, Verma U (2021) Weed density and distribution estimation for precision agriculture using semi-supervised learning. IEEE Access 9:27971–27986. https://doi.org/10.1109/ACCESS.2021.3057912
Szegedy C, Ioffe S, Vanhoucke V, and Alemi AA (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: 31st AAAI conference on artificial intelligence, 4278–4284
Wu X, Aravecchia S, Lottes P, Stachniss C, Pradalier C (2020) Robotic weed control using automated weed and crop classification. J Field Robot 37:322
Xu Y, He R, Gao Z, Li C, Zhai Y, Jiao Y (2020) Weed density detection method based on absolute feature corner points in field. Agronomy 10(1):113. https://doi.org/10.3390/agronomy10010113
Yu J, Schumann AW, Cao Z, Sharpe SM, Boyd NS (2019a) Weed detection in perennial ryegrass with deep learning convolutional neural network. Front Plant Sci 10:1–9. https://doi.org/10.3389/fpls.2019.01422
Yu J, Sharpe SM, Schumann AW, Boyd NS (2019b) Deep learning for image-based weed detection in turfgrass. Eur J Agron 104:78–84. https://doi.org/10.1016/j.eja.2019b.01.004
Zheng Y-Y, Kong J-L, Jin X-B, Wang X-Y, Su T-L, Zuo M (2019) Crop deep: the crop vision dataset for deep-learning-based classification and detection in precision agriculture. Sensors 19(5):1058. https://doi.org/10.3390/S19051058
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The authors thank the editors and anonymous reviewers for providing helpful suggestions to improve the quality of this manuscript. This research received no specific grant from any funding agency in the commercial, public, or not-for-profit organizations.
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Mishra, A.M., Harnal, S., Gautam, V. et al. Weed density estimation in soya bean crop using deep convolutional neural networks in smart agriculture. J Plant Dis Prot 129, 593–604 (2022). https://doi.org/10.1007/s41348-022-00595-7
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DOI: https://doi.org/10.1007/s41348-022-00595-7