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Sustainable Oil Palm Resource Assessment Based on an Enhanced Deep Learning Method

Author

Listed:
  • Xinni Liu

    (School of Information, Xi’an University of Finance and Economics, Xi’an 710100, China)

  • Kamarul H. Ghazali

    (Faculty of Electrical and Electronic Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Malaysia)

  • Akeel A. Shah

    (Key Laboratory of Low-Grade Energy Utilization Technologies and Systems, MOE, Chongqing University, Chongqing 400030, China)

Abstract

Knowledge of the number and distribution of oil palm trees during the crop cycle is vital for sustainable management and predicting yields. The accuracy of the conventional image processing method is limited for the hand-crafted feature extraction method and the overfitting problem occurs due to the insufficient dataset. We propose a modification of the Faster Region-based Convolutional Neural Network (FRCNN) for palm tree detection to reduce the overfitting problem and improve the detection accuracy. The enhanced FRCNN (EFRCNN) leads to improved performance for detecting objects (in the same image) when they are of multiple sizes by using a feature concatenation method. Transfer learning based on a ResNet50 model is used to extract the features of the input image. High-resolution images of oil palm trees from a drone are used to form the data set, containing mature, young, and mixed oil palm tree regions. We train and test the EFRCNN, the FRCNN, a CNN used recently for oil palm image detection, and two standard methods, namely, the support vector machine (SVM) and template matching (TM). The results reveal an overall accuracy of ≥96.8% for the EFRCNN on the three test sets. The accuracy is higher than the CNN and FRCNN and substantially higher than SVM and TM. For large-scale plantations, the accuracy improvement is significant. This research provides a method for automatically counting the oil palm trees in large-scale plantations.

Suggested Citation

  • Xinni Liu & Kamarul H. Ghazali & Akeel A. Shah, 2022. "Sustainable Oil Palm Resource Assessment Based on an Enhanced Deep Learning Method," Energies, MDPI, vol. 15(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4479-:d:842897
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    References listed on IDEAS

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    1. Bipul Neupane & Teerayut Horanont & Nguyen Duy Hung, 2019. "Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-22, October.
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