Short communication impact of environmental factors on the larval population of bagworm, Metisa plana WALKER (LEPIDOPTERA: PSYCHIDAE) in oil palm smallholdings

Infestations and outbreaks of bagworms, Metisa plana Walker (Lepidoptera:Psychidae) have been reported primarily in Peninsular Malaysia for many years. Bagworm infestation is a significant problem primarily due to the mismanagement and lack of proper monitoring in the field, which later affected the yield and profit loss, especially among smallholders. Understanding the impact of environmental conditions on bagworms may assist in a more proper pest control strategy. Changes in environmental conditions have the potential to interrupt the bagworm's life cycle. This study investigated the effects of temperature, humidity, and rainfall on the bagworm population in selected smallholding oil planted areas in Johor. Anemometer was used to collect the data of temperature and humidity while the collection of rainfall was taken from Meteorology Station. The results showed that temperature (r= 0.211) and rainfall (r= 0.108) have minimal effects on the bagworm population, recording positive relationships with Metisa plana. Relative humidity (r= -0.203), however, showed a negative correlation with M. plana. The findings suggested that temperature and rainfall may affect bagworm populations, but a long-term study is required to comprehend this impact fully. An improve scientific approach should be used in future research along with suitable Integrated Environmental Pest Management (IEPM) techniques, monitoring of the climate and pests, and the use of modelling tools as mitigation strategies to determine the effects of environmental factors in bagworms

Oil nano-emulsion formulation of Azadirachtin for control of Bemisia tabaci gennadius

Current water emulsion insecticides only provide limited control of Bemisia tabaci. Oil droplets were found to be more effective as they spread much better on leaf surfaces compared to either water alone or water that contained adjuvant. Thus oil nano-emulsion formulation derived from azadirachtin was developed as an effort to control the population of whiteflies, B. tabaci. Oil nano-emulsion system was developed for insecticide formulations by constructing ternary phase diagrams with 70% (w/w) emulsion system constituted of non-ionic surfactant(s), carrier, water, and 30% (w/w) neem oil as an active ingredient. The non-ionic surfactant was alkylpolyglucosides while carrier or oil phase was dimethylamide. Ternary phase diagrams of the mixed surfactant systems MBL510H: MBL530B at mixed surfactant ratios (MSRs) of 5:5, 6:4, 7:3, 8:2, 9:1 exhibited larger isotropic (I) phase than the single surfactants of either MBL510H or MBL530B. The points were selected from the ‘I’ phase and homogenous region for preformulation. Most of the points selected were from regions with high proportion of oil, low proportion of water and adequate proportion of surfactant to mix with active ingredient and to form water-in-oil (W/O) emulsion. Sixteen formulations miscible with neem oil were selected. In the stability study, all the selected formulations were stable under centrifugation and storage at room temperature (25˚C). However, at 54°C after 14 days storage, F3, F7, F9, F10, and F12 showed phase separation,transformed to two opaque phases. The mean particle size of nano-emulsions ranged between 150.00 and 450.00nm except for F9 with mean particle size of 640.44nm. All sixteen formulations showed surface tension lower than water (72.00mN/m). The formulation F14 (29.90mN/m), F15 (29.93mN/m) and F16 (29.86mN/m) showed lower surface tension compared to other formulations. The zeta potential values of F14 (39.60mV), F15 (39.20mV) and F16 (38.80mV) were higher compared to the other formulations. The value is related to the stability of colloidal dispersions and high zeta potential value will confer stability. In the biological activity study, the adult B. tabaci were used to test the toxicity of the oil nano-emulsion formulation. The result showed the mortality of the adults was higher with the increase of time exposure. The mortality rate of B. tabaci showed that the oil nano-emulsion formulations gave excellent efficacy with LC50 value of 3.70ppm at 96 h after treatment. In the measurement of spread area study, three different levels of formulation toxicities were used to determine the spreading coefficient and evaluate the mode of action of the formulation on the early nymphal instar’s B. tabaci. The studies have proved the interaction between spread area and mortality rate. The larger the spread area of the droplet result in increased of mortality. In this study, F15 formulation with low mean lethal concentration gave the larger spread area on the leaves surfaces. As a result, the formulation also gave highest mortality rate on early nymphal instar of whiteflies due to the spreading ability of this formulation. This finding has proved the mode of action of oil nanoemulsion formulation in killing the early nymphal instars of B.tabaci by giving wider coverage of active material on leaves surface and brings larger areas of cuticle into contact with the insecticides, resulting in better retention and enhanced the biological effect

Oil based nano-emulsion formulation of azadiracthin for biopesticide

A broad spectrum plant based formulation for killing insects includes an active ingredient extracted from a plant material, a surfactant in an effective amount to provide emulsification of the formulation and reducing surface tension of the formulation and a carrier for the active ingredient, the carrier includes an insecticidal oil-containing amide

Impact of multiple aerial spraying of Bacillus thuringiensis on bagworm control in oil palm smallholdings in Johor, Malaysia

The outbreak of bagworm has been a severe threat, causing significant loss to the oil palm industry in Johor, Malaysia. This study investigated the impact of multiple aerial applications of Bacillus thuringiensis (Bt)-based biopesticides in controlling the bagworm outbreak at two smallholdings in Johor, namely Smallholding A (Sh.A) and Smallholding B (Sh.B). Two types of agricultural aircrafts used in this study were Grumman G-164 A Super AgCat and M-18 Dromader. The results showed that a significant reduction of bagworms (83.5%) was recorded after the third round of aerial spray in Sh.A. Whilst, Sh.B recorded a significant reduction of bagworms (83.5%) after the fourth round of aerial spray. The result indicated that multiple applications of Bt aerial spray at the precise timing and strategy based on the bagworm’s life cycle are crucial in ensuring the application’s effectiveness in bringing down the bagworm population to below the economic threshold level (ETL). A census conducted in 2019 and 2020 recorded that the bagworm population in both areas maintained below the threshold level even after more than three years of application. With the implementation of a long-term Integrated Pest Management (IPM) strategy, such as planting beneficial plants, the bagworm population can be maintained under ETL even after years of aerial sprays

Identification of bagworm (Metisa plana) instar stages using hyperspectral imaging and machine learning techniques

A serious outbreak of leaf-eating insects namely bagworm (Lepidoptera: Psychidae), especially 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. The capability of spectral reflectance and morphological features namely area, perimeter, major axis length, and minor axis length to classify the instar stage were studied. A total of 2000 sample points of larva were extracted from hyperspectral images. It was then followed by the identification of sensitive wavelengths of each stage using analysis of variance (ANOVA). 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. The capability of the model to detect the young larval instar stages (S2 - S3) where an active feeding activity takes place allows quick decisions about outbreak control measures

Automatic Classification of Bagworm, <i>Metisa plana</i> (Walker) Instar Stages Using a Transfer Learning-Based Framework

Bagworms, particularly Metisa plana Walker (Lepidoptera: Psychidae), are one of the most destructive leaf-eating pests, especially in oil palm plantations, causing severe defoliation which reduces yield. Due to the delayed control of the bagworm population, it was discovered to be the most widespread oil palm pest in Peninsular Malaysia. Identification and classification of bagworm instar stages are critical for determining the current outbreak and taking appropriate control measures in the infested area. Therefore, this work proposes an automatic classification of bagworm larval instar stage starting from the second (S2) to the fifth (S5) instar stage using a transfer learning-based framework. Five different deep CNN architectures were used i.e., VGG16, ResNet50, ResNet152, DenseNet121 and DenseNet201 to categorize the larval instar stages. All the models were fine-tuned using two different optimizers, i.e., stochastic gradient descent (SGD) with momentum and adaptive moment estimation (Adam). Among the five models used, the DenseNet121 model, which used SGD with momentum (0.9) had the best classification accuracy of 96.18% with a testing time of 0.048 s per sample. Besides, all the instar stages from S2 to S5 can be identified with high value accuracy (94.52–97.57%), precision (89.71–95.87%), sensitivity (87.67–96.65%), specificity (96.51–98.61%) and the F1-score (88.89–96.18%). The presented transfer learning approach yields promising results, demonstrating its ability to classify bagworm instar stages