Skip to main content
SpringerLink
Log in

An efficient deep learning with a big data-based cotton plant monitoring system

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

In Agriculture, plant monitoring plays an important role from seedling to harvesting which helps farmers achieve a good yield. This paper focuses on building bigdata based cotton plant monitoring system. To build this system, the plant images are collected from the agricultural field with an Android mobile App. The collected data are labeled using a web-based feature labeling application. After applying the pre-trained Deep-Learning (DL) classification algorithm to the labeled images, the farmers benefited from the subsequent information. This paper discusses different pre-trained Convolutional Neural Network (CNN) architectures such as ResNet18, GoogLeNet, InceptionV3, and MobileNetV3 Large used to monitor the health of the cotton plant. In plant health monitoring, the classification and identification accuracy are improved with better feature extraction. In comparison with other methods employed in this paper, MobileNetV3Large provides high accuracy. The proposed model classifies 11 different cotton plant regions which are boll, bud, crown, flower, land, leaf, stem, unhealthy leaf, weed, young boll, and young leaf. The MobileNetV3Large model offers an accuracy, specificity, and precision value of 93.9%, 96.12%, and 97.48% when evaluated using the images obtained from smartphones. The smart application developed also provides information to the framers regarding harvesting and yield. The proposed model is determined in real-world applications to identify whether a plant sprouted is a cotton plant or a weed. Next, it can also identify the health condition of the cotton plant and can predict the type of disease identified.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Code availability

Not applicable.

References

  1. Rao BSS, Rao BS (2023) An effective WBC segmentation and classification using MobilenetV3–ShufflenetV2 based deep learning framework. IEEE Access 11:27739–27748. https://doi.org/10.1109/ACCESS.2023.3259100

    Article  Google Scholar 

  2. Yang Y, Zhang L, Du M, Bo J, Liu H, Ren L, Li X, Deen MJ (2021) A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions. Comput Biol Med 139:104887. https://doi.org/10.1016/j.compbiomed.2021.104887

    Article  Google Scholar 

  3. Alimboyong CR, Hernandez AA (2019) An improved deep neural network for classification of plant seedling images. In: 2019 IEEE 15th International Colloquium on signal processing & its applications (CSPA). IEEE, pp 217–222. https://doi.org/10.1109/CSPA.2019.8696009

  4. Bhatheja H, Jayanthi N (2021) Detection of cotton plant disease for fast monitoring using enhanced deep learning technique. In: 2021 5th International Conference on electrical, electronics, communication, computer technologies and optimization techniques (ICEECCOT). IEEE, pp 820–825. https://doi.org/10.1109/ICEECCOT52851.2021.9708045

  5. Zambare R, Awati C, Deshmukh R, Shirgave S (2022) A theoretical study on cnn based cotton crop disease detection. In: 2022 6th International Conference on trends in electronics and informatics (ICOEI). IEEE, pp 1062–1066. https://doi.org/10.1109/ICOEI53556.2022.9777219

  6. Chavan TR, Nandedkar AV (2018) AgroAVNET for crops and weeds classification: a step forward in automatic farming. Comput Electron Agric 154:361–372. https://doi.org/10.1016/j.compag.2018.09.021

    Article  Google Scholar 

  7. Trong VH, Gwang-hyun Y, Vu DT, Jin-young K (2020) Late fusion of multimodal deep neural networks for weeds classification. Comput Electron Agric 175:105506. https://doi.org/10.1016/j.compag.2020.105506

    Article  Google Scholar 

  8. Rahman NR, Hasan MAM, Shin J (2020) Performance comparison of different convolutional neural network architectures for plant seedling classification. In: 2020 2nd International Conference on advanced information and communication technology (ICAICT). IEEE, pp 146–150. https://doi.org/10.1109/ICAICT51780.2020.9333468

  9. Hasan AM, Sohel F, Diepeveen D, Laga H, Jones MG (2021) A survey of deep learning techniques for weed detection from images. Comput Electron Agric 184:106067. https://doi.org/10.1016/j.compag.2021.106067

    Article  Google Scholar 

  10. Jasiński M, Mączak J, Szulim P, Radkowski S (2018) Autonomous agricultural robot-testing of the vision system for plants/weed classification. In: Automation 2018: advances in automation, robotics and measurement techniques. Springer International Publishing, pp 473–482. 10.1007/978-3-319-77179-3_44.

  11. Zaman MHM, Mustaza SM, Ibrahim MF, Zulkifley MA, Mustafa MM (2021) Weed classification based on statistical features from Gabor transform magnitude. In: 2021 International Conference on decision aid sciences and application (DASA). IEEE, pp 147–151. https://doi.org/10.1109/DASA53625.2021.9681930

  12. Aptoula E (2021) Weed and crop classification with domain adaptation for precision agriculture. In: 2021 29th signal processing and communications applications conference (SIU). IEEE, pp 1–4. https://doi.org/10.1109/SIU53274.2021.9477948

  13. Crescitelli A, Consales M, Cutolo A, Cusano A, Penza M, Aversa P, Giordano M (2008) Novel sensitive nanocoatings based on SWCNT composites for advanced fiber optic chemo-sensors. In: SENSORS, 2008 IEEE. IEEE, pp 965–968. https://doi.org/10.1109/ICSENS.2008.4716602

  14. Jin X, Che J, Chen Y (2021) Weed identification using deep learning and image processing in vegetable plantation. IEEE Access 9:10940–10950. https://doi.org/10.1109/ACCESS.2021.3050296

    Article  Google Scholar 

  15. Gothai E, Natesan P, Aishwariya S, Aarthy TB, Singh GB (2020) Weed identification using convolutional neural network and convolutional neural network architectures. In: 2020 Fourth International Conference on computing methodologies and communication (ICCMC). IEEE, pp 958–965. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000178

  16. Armstrong DR, Götz M, Nardelli VC, de Oliveira Gomes VE (2021) Machine learning for identification and classification of crops and weeds. In: 2021 XI Brazilian Symposium on computing systemsengineering (SBESC). IEEE, pp 1–7. https://doi.org/10.1109/SBESC53686.2021.9628296

  17. Chen D, Lu Y, Li Z, Young S (2022) Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems. Comput Electron Agric 198:107091. https://doi.org/10.1016/j.compag.2022.107091

    Article  Google Scholar 

  18. Chen P, Xiao Q, Zhang J, Xie C, Wang B (2020) Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation. Comput Electron Agric 176:105612. https://doi.org/10.1016/j.compag.2020.105612

    Article  Google Scholar 

  19. Sapkal AT, Kulkarni UV (2018) Comparative study of leaf disease diagnosis system using texture features and deep learning features. Int J Appl Eng Res 13(19):14334–14340

    Google Scholar 

  20. Song S, Lam JC, Han Y, Li VO (2020) ResNet-LSTM for real-time PM 2.5 and PM10 estimation using sequential smartphone images. IEEE Access 8:220069–220082. https://doi.org/10.1109/ACCESS.2020.3042278

    Article  Google Scholar 

  21. Shi B, Zhang X, Wang Z, Song J, Han J, Zhang Z, Toe TT (2022) GoogLeNet-based diabetic-retinopathy-detection. In: 2022 14th International Conference on advanced computational intelligence (ICACI). IEEE, pp 246–249. https://doi.org/10.1109/ICACI55529.2022.9837677

  22. Singh N, Tewari VK, Biswas PK, Pareek CM, Dhruw LK (2021) Image processing algorithms for in-field cotton boll detection in natural lighting conditions. Artif Intell Agric 5:142–156. https://doi.org/10.1016/j.aiia.2021.07.002

    Article  Google Scholar 

  23. Arjunagi S, Patil NB (2023) Optimized convolutional neural network for identification of maize leaf diseases with adaptive ageist spider monkey optimization model. Int J Inform Technol 15(2):877–891. https://doi.org/10.1007/s41870-021-00657-3

    Article  Google Scholar 

  24. El Bourakadi D, Ramadan H, Yahyaouy A, Boumhidi J (2023) A novel solar power prediction model based on stacked BiLSTM deep learning and improved extreme learning machine. Int J Inform Technol 15(2):587–594. https://doi.org/10.1007/s41870-022-01118-1

    Article  Google Scholar 

  25. Nakra A, Duhan M (2023) Deep neural network with harmony search based optimal feature selection of EEG signals for motor imagery classification. Int J Inform Technol 15(2):611–625. https://doi.org/10.1007/s41870-021-00857-x

    Article  Google Scholar 

  26. Singh R, Agarwal BB (2023) An automated brain tumor classification in MR images using an enhanced convolutional neural network. Int J Inform Technol 15(2):665–674. https://doi.org/10.1007/s41870-022-01095-5

    Article  Google Scholar 

  27. Jain A, Ratnoo S, Kumar D (2020) A novel multi-objective genetic algorithm approach to address class imbalance for disease diagnosis. Int J Inform Technol. https://doi.org/10.1007/s41870-020-00471-3

    Article  Google Scholar 

  28. Lamba S, Baliyan A, Kukreja V (2023) A novel GCL hybrid classification model for paddy diseases. Int J Inform Technol 15(2):1127–1136. https://doi.org/10.1007/s41870-022-01094-6

    Article  Google Scholar 

  29. Gaikwad SS, Rumma SS, Hangarge M (2022) Fungi affected fruit leaf disease classification using deep CNN architecture. Int J Inform Technol 14(7):3815–3824. https://doi.org/10.1007/s41870-022-00860-w

    Article  Google Scholar 

  30. Upadhyay SK, Kumar A (2022) A novel approach for rice plant diseases classification with deep convolutional neural network. Int J Inform Technol. https://doi.org/10.1007/s41870-021-00817-5

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Artificial Intelligence and Machine Learning, St. Joseph’s College of Engineering, OMR, Kamaraj Nagar, Semmancheri, Chennai, Tamil Nadu, India

    Ancy Stephen

  2. Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu, India

    Punitha Arumugam

  3. Department of Computer Science and Engineering, St. Joseph’s College of Engineering, OMR, Kamaraj Nagar, Semmancheri, Chennai, 600119, Tamil Nadu, India

    Chandrasekar Arumugam

Authors
  1. Ancy Stephen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Punitha Arumugam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chandrasekar Arumugam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ancy Stephen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants.

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stephen, A., Arumugam, P. & Arumugam, C. An efficient deep learning with a big data-based cotton plant monitoring system. Int. j. inf. tecnol. 16, 145–151 (2024). https://doi.org/10.1007/s41870-023-01536-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-023-01536-9

Keywords

Search

Navigation