Progress in Agricultural Engineering Sciences
Volume/Issue:
Volume 20: Issue 1
E2E model from crop suggestion to harvesting time prediction in sugarcane plant utilizing machine learning and deep learning
Authors:
P.E. Rubini IBM Private Ltd., India

Search for other papers by P.E. Rubini in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0003-0238-6640
and
P. Kavitha Department of Computer Science Engineering, CMR Institute of Technology, Visvesvaraya Technological University, Bengaluru, Karnataka 560037, India

Search for other papers by P. Kavitha in
Current site
Google Scholar
PubMed Close
Pages:
233–270
Online Publication Date:
09 Dec 2024
Publication Date:
12 Dec 2024
Article Category:
Research Article
DOI:
https://doi.org/10.1556/446.2024.00139
Keywords:
crop yield; sugarcane plant; growth quality; soil fertilizer; weed detection; disease diagnosis; harvesting time
Restricted access

Abstract

The automated system for enhancing plant growth presents an innovative approach to optimize quality of sugarcane cultivation for four main sugarcane growing zones. It includes issues like recommendation of crops based on soil nutrients, diagnosis of disease in the leaf and stem images of sugarcane, weed detection and harvesting time prediction. The research work proposed in the article presents an innovative two-stage approach for object detection and classification in agricultural imagery. Initially, YOLOv8 (You Only Look Once) is employed to accurately detect objects within images, delineating them with precise boundary boxes. Subsequently, the focus of hybrid model integrating Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, known as Contextual Long Short-Term Memory (CLSTM), is employed. This dual-stage methodology harnesses the speed and accuracy of YOLOv8 for robust object localization, while the CLSTM model ensures nuanced classification, contributing to comprehensive and accurate approach for object detection and crop-weed differentiation in agricultural scenarios. The proposed approach is compared with the four DL algorithms for identifying weeds in sugarcane crops and subsequently assessed their accuracy and F1 score performance. At a learning rate of 0.002, the findings of CLSTM showcase superior precision at 98.5%, recall at 97.8%, F1 score at 98.1%, and an overall accuracy of 97.7%. The subsequent task is harvesting time prediction, which entails identifying the best time to harvest sugarcane based on the planting period, weather predictions, and sugarcane brix value. The implementation of this automated system not only enhances the productivity of sugarcane cultivation but also serves as a model for sustainable and resource-efficient agriculture.

  • Ahmad, A., Saraswat, D., Aggarwal, V., Etienne, A., and Hancock, B. (2021). Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems. Computers and Electronics in Agriculture, 184: 106081.

    • Search Google Scholar
    • Export Citation

  • Amarasingam, N., Gonzalez, F., Salgadoe, A.S.A., Sandino, J., and Powell, K. (2022). Detection of white leaf disease in sugarcane crops using UAV-derived RGB imagery with existing deep learning models. Remote Sensing, 14(23): 6137.

    • Search Google Scholar
    • Export Citation

  • Bandi, R., Swamy, S., and Arvind, C.S. (2023). Leaf disease severity classification with explainable artificial intelligence using transformer networks. International Journal of Advanced Technology and Engineering Exploration, 10(100): 278.

    • Search Google Scholar
    • Export Citation

  • Deshmukh, M., Jaiswar, A., Joshi, O., and Shedge, R. (2022). Farming assistance for soil fertility improvement and crop prediction using XGBoost. In: ITM Web of Conferences, Vol. 44, 03022.

    • Search Google Scholar
    • Export Citation

  • El-Kenawy, E.S.M., Khodadadi, N., Mirjalili, S., Makarovskikh, T., Abotaleb, M., Karim, F.K., Alkahtani, H.K., Abdelhamid, A.A., Eid, M.M., Horiuchi, T., and Ibrahim, A. (2022). Metaheuristic optimization for improving weed detection in wheat images captured by drones. Mathematics, 10(23): 4421.

    • Search Google Scholar
    • Export Citation

  • Fu, Y., Gao, H., Yu, H., Yang, Q., Peng, H., Liu, P., Li, Y., Hu, Z., Zhang, R., Li, J., and Qi, Z. (2022). Specific lignin and cellulose depolymerization of sugarcane bagasse for maximum bioethanol production under optimal chemical fertilizer pretreatment with hemicellulose retention and liquid recycling. Renewable Energy, 200: 1371–1381.

    • Search Google Scholar
    • Export Citation

  • Gunjan, V.K., Kumar, S., Ansari, M.D., and Vijayalata, Y. (2022). Prediction of agriculture yields using machine learning algorithms. In: Proceedings of the 2nd international conference on recent trends in machine learning, IoT, smart cities and applications: ICMISC 2021, pp. 17–26.

    • Search Google Scholar
    • Export Citation

  • Gupta, M., BV, S.K., Kavyashree, B., Narapureddy, H.R., Surapaneni, N., and Varma, K. (2022, February). Various crop yield prediction techniques using machine learning algorithms. In: 2022 second international conference on artificial intelligence and smart energy (ICAIS), pp. 273–279.

    • Search Google Scholar
    • Export Citation

  • Haq, M.A. (2022). CNN based automated weed detection system using UAV imagery. Computer Systems Science & Engineering, 42(2).

  • Jin, X., Che, J., and Chen, Y. (2021). Weed identification using deep learning and image processing in vegetable plantation. IEEE Access, 9: 10940–10950.

    • Search Google Scholar
    • Export Citation

  • Johnson, R.M., Orgeron, A.J., Spaunhorst, D.J., Huang, I.S., and Zimba, P.V. (2023). Discrimination of weeds from sugarcane in Louisiana using hyperspectral leaf reflectance data and pigment analysis. Weed Technology, 37(2): 123–131.

    • Search Google Scholar
    • Export Citation

  • Le, V.N.T., Apopei, B., and Alameh, K. (2019). Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods. Information processing in agriculture, 6(1): 116–131.

    • Search Google Scholar
    • Export Citation

  • Manavalan, R. (2021). Efficient detection of sugarcane diseases through intelligent approaches: a review. Asian Journal of Research and Review in Agriculture: 174–184.

    • Search Google Scholar
    • Export Citation

  • Manikandakumar, M. and Karthikeyan, P. (2023). Weed classification using particle swarm optimization and deep learning models. Comput. Syst. Sci. Eng, 44(1): 913–927.

    • Search Google Scholar
    • Export Citation

  • Militante, S.V. and Gerardo, B.D. (2019). December. Detecting sugarcane diseases through adaptive deep learning models of convolutional neural network. In: 2019 IEEE 6th international conference on engineering technologies and applied sciences (ICETAS), pp. 1–5.

    • Search Google Scholar
    • Export Citation

  • Militante, S.V., Gerardo, B.D., and Medina, R.P. (2019 October). Sugarcane disease recognition using deep learning. In: 2019 IEEE Eurasia conference on IOT, communication and engineering (ECICE), pp. 575–578.

    • Search Google Scholar
    • Export Citation

  • Modi, R.U., Kancheti, M., Subeesh, A., Raj, C., Singh, A.K., Chandel, N.S., Dhimate, A.S., Singh, M.K., and Singh, S. (2023). An automated weed identification framework for sugarcane crop: a deep learning approach. Crop Protection, 173: 106360.

    • Search Google Scholar
    • Export Citation

  • Narmilan, A., Gonzalez, F., Salgadoe, A.S.A., and Powell, K. (2022). Detection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images. Drones, 6(9): 230.

    • Search Google Scholar
    • Export Citation

  • Panakkal, E.J., Sriariyanun, M., Ratanapoompinyo, J., Yasurin, P., Cheenkachorn, K., Rodiahwati, W., and Tantayotai, P. (2022). Influence of sulfuric acid pretreatment and inhibitor of sugarcane bagasse on the production of fermentable sugar and ethanol. Applied Science and Engineering Progress, 15(1).

    • Search Google Scholar
    • Export Citation

  • Patra, P.S., Adhikary, P., Kheroar, S., Tamang, A., Sinha, A.C., and Mahato, D. (2017). Direct and residual effect of organics on groundnut – maize cropping sequence. Research Journal of Agricultural Sciences, 8(2): 411–416, https://www.rjas.or.

    • Search Google Scholar
    • Export Citation

  • Raja, R., Nguyen, T.T., Slaughter, D.C., and Fennimore, S.A. (2020). Real-time weed-crop classification and localisation technique for robotic weed control in lettuce. Biosystems Engineering, 192: 257–274.

    • Search Google Scholar
    • Export Citation

  • Rubini, P.E. and Kavitha, P. (2022). Prediction of the right crop for the right soil and recommendation of fertiliser usage by machine learning algorithm. Int. J. Computer Applications in Technology, 69(2).

    • Search Google Scholar
    • Export Citation

  • Rubini, P.E. and Kavitha, P. (2023). A deep learning-based approach for early detection of disease in sugarcane plants: an explainable artificial intelligence model. IAES International Journal of Artificial Intelligence (IJ-AI), 13(1).

    • Search Google Scholar
    • Export Citation

  • Sarvini, T., Sneha, T., GS, S.G., Sushmitha, S., and Kumaraswamy, R. (2019 April). Performance comparison of weed detection algorithms. In: 2019 international conference on communication and signal processing (ICCSP), pp. 0843–0847.

    • Search Google Scholar
    • Export Citation

  • Senthil Kumar, C. and Vijay Anand, R. (2024). Energy-efficient cluster head using modified fuzzy logic with WOA and path selection using enhanced CSO in IoT-enabled smart agriculture systems. The Journal of Supercomputing, 80: 11149–11190.

    • Search Google Scholar
    • Export Citation

  • Shingade, S.D. and Mudhalwadkar, R.P. (2023a). Sensor information-based crop recommendation system using machine learning for the fertile regions of Maharashtra. Concurrency Computat Pract Exper, 35(23): e7774, https://doi.org/10.1002/cpe.7774.

    • Search Google Scholar
    • Export Citation

  • Shingade, S.D. and Mudhalwadkar, R.P. (2023b). Analysis of crop prediction models using data analytics and ML techniques: a review. Multimedia Tools and Applications: 1–26.

    • Search Google Scholar
    • Export Citation

  • Sunil, G.C., Zhang, Y., Koparan, C., Ahmed, M.R., Howatt, K., and Sun, X. (2022). Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions. Journal of Agriculture and Food Research, 9: 100325.

    • Search Google Scholar
    • Export Citation

  • Sushil, S.J. (2023). Biochemical study of freshwater fish clarias batrachus (l.) infected with cestode parasite, lytocestus sp. From District Jalgaon, india, International Journal of Biological Innovations, 5(2): 50–54, http://ijbi.org.in | http://www.gesa.org.in/journals.php.

    • Search Google Scholar
    • Export Citation

  • Tamilvizhi, T., Surendran, R., Anbazhagan, K., and Rajkumar, K. (2022). Quantum behaved particle swarm optimization-based deep transfer learning model for sugarcane leaf disease detection and classification. Mathematical Problems in Engineering, 2022.

    • Search Google Scholar
    • Export Citation

  • Tanwar, V., Lamba, S., Sharma, B., and Sharma, A. (2023, March). Red Rot disease prediction in sugarcane using the deep learning approach. In: 2023 2nd international conference for innovation in technology (INOCON), pp. 1–5.

    • Search Google Scholar
    • Export Citation

  • Wang, Q., Zhang, Q., Zhang, Y., Zhou, G., Li, Z., and Chen, L. (2022, January). Lodged sugarcane/crop dividers interaction: analysis of robotic sugarcane harvester in agriculture via a rigid-flexible coupled simulation method. In Actuators, 11(1): 23.

    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
Cover Progress in Agricultural Engineering Sciences
Progress in Agricultural Engineering Sciences
Print ISSN:
1786-335X
Online ISSN:
1787-0321

Senior editors

Editor(s)-in-Chief: Felföldi, József

Chair of the Editorial Board Szendrő, Péter

Editorial Board

  • Beke, János (Szent István University, Faculty of Mechanical Engineerin, Gödöllő – Hungary)
  • Fenyvesi, László (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Szendrő, Péter (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Felföldi, József (Szent István University, Faculty of Food Science, Budapest – Hungary)

 

Advisory Board

  • De Baerdemaeker, Josse (KU Leuven, Faculty of Bioscience Engineering, Leuven - Belgium)
  • Funk, David B. (United States Department of Agriculture | USDA • Grain Inspection, Packers and Stockyards Administration (GIPSA), Kansas City – USA
  • Geyer, Martin (Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Horticultural Engineering, Potsdam - Germany)
  • Janik, József (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Kutzbach, Heinz D. (Institut für Agrartechnik, Fg. Grundlagen der Agrartechnik, Universität Hohenheim – Germany)
  • Mizrach, Amos (Institute of Agricultural Engineering. ARO, the Volcani Center, Bet Dagan – Israel)
  • Neményi, Miklós (Széchenyi University, Department of Biosystems and Food Engineering, Győr – Hungary)
  • Schulze-Lammers, Peter (University of Bonn, Institute of Agricultural Engineering (ILT), Bonn – Germany)
  • Sitkei, György (University of Sopron, Institute of Wood Engineering, Sopron – Hungary)
  • Sun, Da-Wen (University College Dublin, School of Biosystems and Food Engineering, Agriculture and Food Science, Dublin – Ireland)
  • Tóth, László (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)

Prof. Felföldi, József
Institute: MATE - Hungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology, Department of Measurements and Process Control
Address: 1118 Budapest Somlói út 14-16
E-mail: felfoldi.jozsef@uni-mate.hu

Indexing and Abstracting Services:

  • CABI
  • ERIH PLUS
  • SCOPUS

2023  
Scopus  
CiteScore 1.8
CiteScore rank Q2 (General Agricultural and Biological Sciences)
SNIP 0.497
Scimago  
SJR index 0.258
SJR Q rank Q3

Progress in Agricultural Engineering Sciences
Publication Model Hybrid
Submission Fee none
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Article Processing Charge 900 EUR/article (only for OA publications)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2025 Online subsscription: 173 EUR / 190 USD
Print + online subscription: 200 EUR / 220 USD
Subscription Information Online subscribers are entitled access to all back issues published by Akadémiai Kiadó for each title for the duration of the subscription, as well as Online First content for the subscribed content.
Purchase per Title Individual articles can be purchased at the prices indicated.

Progress in Agricultural Engineering Sciences
Language English
Size B5
Year of
Foundation		 2004
Volumes
per Year		 1
Issues
per Year		 1
Founder Magyar Tudományos Akadémia  
Founder's
Address		 H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address		 H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher		 Chief Executive Officer, Akadémiai Kiadó
ISSN 1786-335X (Print)
ISSN 1787-0321 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Aug 2024 0 0 0
Sep 2024 0 0 0
Oct 2024 0 0 0
Nov 2024 0 0 0
Dec 2024 38401 11 8
Jan 2025 47820 7 2
Feb 2025 0 0 0