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Comparative approach on crop detection using machine learning and deep learning techniques

Author

Listed:
  • V. Nithya

    (Dr. M.G.R. Educational and Research Institute)

  • M. S. Josephine

    (Dr. M.G.R. Educational and Research Institute)

  • V. Jeyabalaraja

    (Velammal Engineering College)

Abstract

Agriculture is an expanding area of study. Crop prediction in agriculture is highly dependent on soil and environmental factors, such as rainfall, humidity, and temperature. Previously, farmers had the authority to select the crop to be farmed, oversee its development, and ascertain the optimal harvest time. The farming community is facing challenges in sustaining its practices due to the swift alterations in climatic conditions. Therefore, machine learning algorithms have replaced traditional methods in predicting agricultural productivity in recent years. To guarantee optimal precision through a specific machine learning approach. Authors extend their approach not limited to Machine Learning but also with Deep Learning Techniques. We use machine and deep learning algorithms to predict crop outcomes accurately. In this proposed model, we utilise machine learning algorithms such as Naive Bayes, decision tree, and KNN. It is worth noting that the decision tree algorithm demonstrates superior performance compared to the other algorithms, achieving an accuracy rate of 83%. In order to enhance the precision, we have suggested implementing a deep learning technique, specifically a convolutional neural network, to identify the crops. Achieving an accuracy of 93.54% was made possible by implementing this advanced deep-learning model.

Suggested Citation

  • V. Nithya & M. S. Josephine & V. Jeyabalaraja, 2024. "Comparative approach on crop detection using machine learning and deep learning techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4636-4648, September.
  • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:9:d:10.1007_s13198-024-02483-9
    DOI: 10.1007/s13198-024-02483-9
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    References listed on IDEAS

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    1. Sandeep Kumar & Arpit Jain & Anand Prakash Shukla & Satyendr Singh & Rohit Raja & Shilpa Rani & G. Harshitha & Mohammed A. AlZain & Mehedi Masud, 2021. "A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-18, June.
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