Chili Plant Classification using Transfer Learning models through Object Detection

Authors

  • Amirul Asyraf Abdul Manan Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang
  • Mohd Azraai Mohd Razman Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang
  • Ismail Mohd Khairuddin Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang
  • Muhammad Nur Aiman Shapiee Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang

DOI:

https://doi.org/10.15282/mekatronika.v2i2.6743

Keywords:

Object detection, Precision agriculture, Transfer learning, Crop monitoring, Chili plant

Abstract

This study presents an application of using a Convolutional Neural Network (CNN) based detector to detect chili and its leaves in the chili plant image. Detecting chili on its plant is essential for the development of robotic vision and monitoring. Thus, helps us supervise the plant growth, furthermore, analyses their productivity and quality. This paper aims to develop a system that can monitor and identify bird’s eye chili plants by implementing machine learning. First, the development of methodology for efficient detection of bird’s eye chili and its leaf was made. A dataset of a total of 1866 images after augmentation of bird’s eye chili and its leaf was used in this experiment. YOLO Darknet was implemented to train the dataset. After a series of experiments were conducted, the model is compared with other transfer learning models like YOLO Tiny, Faster R-CNN, and EfficientDet. The classification performance of these transfer learning models has been calculated and compared with each other. The experimental result shows that the Yolov4 Darknet model achieves mAP of 75.69%, followed by EfficientDet at 71.85% for augmented dataset.

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Published

2020-12-12

How to Cite

[1]
A. A. . Abdul Manan, M. A. Mohd Razman, I. Mohd Khairuddin, and M. N. A. Shapiee, “Chili Plant Classification using Transfer Learning models through Object Detection”, MEKATRONIKA, vol. 2, no. 2, pp. 23–27, Dec. 2020.

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Original Article

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