Metisa plana species, may cause a 43 % yield loss in oil palm production due to late proper control of bagworm populations. Identification of the bagworm instar stage is important to ensure proper control measures are applied in the infested area. This study aims to distinguish the bagworm larvae from second (S2) to fifth (S5) instar stages using hyperspectral imaging and machine learning technique. Results show that seven wavelengths from the blue and green band (i.e., 470 nm, 490 nm, 502 nm, 506 nm, 526 nm, 538 nm, and 554 nm) gave the most significant difference in distinguishing the larval instar stages. To provide a more economical approach, only two wavelengths were used for model development. Later, the classifications models were developed separately using five different types of datasets: (A) significant morphological feature, (B) all significant wavelengths, (C) two wavelengths from the same spectral region, (D) two wavelengths from different spectral regions, and (E) two significant wavelengths and a significant morphological feature. Results have shown the dataset which used green bands at 506 nm and 538 nm with a weighted k-nearest neighbour classifier achieved the best value of accuracy (91% – 95%), precision (0.83 – 0.87), sensitivity (0.77 – 0.99), specificity (0.94 – 0.96) and F1-score (0.81 – 0.91). It was mainly due to green pigments which strongly correlates with the chlorophyll content of the frond leaves fed by the larvae to build and enlarge the case.

Figure 1: Top view during the image acquisition of larval sample

Figure 2: (a) Average spectral reflectance of each larval instar stage and its (b) coefficient of variance (CV)

Siti Nurul Afiah Mohd Johari, Siti Khairunniza-Bejo, Abdul Rashid Mohamed Shariff, Nur Azuan Husin, Mohamed Mazmira Mohd Basri and Noorhazwani Kamarudin (2022). Identification of Bagworm (Metisa plana) Instar Stages using Hyperspectral Imaging and Machine Learning Techniques. Computers and Electronics in Agriculture, Volume 194, 2022, 106739.

Full article: https://doi.org/10.1016/j.compag.2022.106739

Date of Input: 01/09/2022 | Updated: 08/06/2023 | ainzubaidah