Metisa Plana Research Articles

Insects found in different ages of coconut viridis variety in Sri Tiga Village, Banyuasin District, South Sumatra, Indonesia

Abstract. Anggraini E, Sinaga TM, Irsan C, Herlinda S, Muslim A, Hamidson H, Sefrila M, Kurnianingsih A, Aryani D, Antoni M, Ikhsan Z. 2024. Insects found in different ages of coconut viridis variety in Sri Tiga Village, Banyuasin District, South Sumatra, Indonesia. Biodiversitas 25: 2637-2647. Coconut (Cocos nucifera L.) is a high-value export commodity and has promising potential on the global market. The existence of insect species is very important in coconut cultivation. The coexistence of insects and plants is interconnected because plants provide habitat and food sources for insects. However, the correlation between plants and insects still needs to be discovered to many people. Therefore, a comprehensive assessment of insect biodiversity in an environment is important to be carried out. This study revealed the structure of the insect community associated with coconut plants and weeds around coconut trees. Therefore, this is a novel and pioneering study that aims to determine the insects associated with coconut plants. This study was conducted by surveying community-owned coconut plantations in Sri Tiga Village, Sumber Marga Telang Sub-district, Banyuasin District, South Sumatera, Indonesia. The observation was carried out during July and September of 2023. The data-gathering process involved identifying three coconut plantations with varying ages of coconut trees (8, 5, and 2 years old). Data collection was carried out using the purposive sampling method by direct observations and using traps such as insect nets, light traps, and pheromone traps. Collected specimens were identified using a microscope in the Laboratory of Entomology, Department of Plant Pests and Disease, Faculty of Agriculture, Universitas Sriwijaya. The results of this study found 12 insect species in coconut trees, namely Oryctes rhinoceros Linnaeus 1758, Aspidiotus sp., Nipaecoccus nipae Maskell 1893, Aleurocanthus sp., Apis cerana Fabricius 1793, Cotesia congregata Say 1836, Dolichoderus thoracicus Smith 1860, Oecophylla smaragdina Fabricus 1775, Provespa sp., Pteroma pendula de Joannis 1929, Metura sp., Metisa plana Walker 1883. The predominant insect pests identified were Aspidiotus sp., N. nipae, and Aleurocanthus sp. Furthermore, the most predominant insects that served as insect scavengers were Polyrhachis sp. and O. smaragdina. Meanwhile, in the weeds around coconut trees, there were found six insect species, namely Aulacophora lewisii Baly 1866, Eumorphus westwoodi Guérin 1858, Mactrotermes sp., Conocephalus sp., Dianemobius sp., and Tettigidea sp.

Transcriptome analysis reveals mechanisms of metabolic detoxification and immune responses following farnesyl acetate treatment in Metisa plana

Metisa plana is a widespread insect pest infesting oil palm plantations in Malaysia. Farnesyl acetate (FA), a juvenile hormone analogue, has been reported to exert in vitro and in vivo insecticidal activity against other insect pests. However, the insecticidal mechanism of FA on M. plana remains unclear. Therefore, this study aims to elucidate responsive genes in M. plana in response to FA treatment. The RNA-sequencing reads of FA-treated M. plana were de novo-assembled with existing raw reads from non-treated third instar larvae, and 55,807 transcripts were functionally annotated to multiple protein databases. Several insecticide detoxification-related genes were differentially regulated among the 321 differentially expressed transcripts. Cytochrome P450 monooxygenase, carboxylesterase, and ATP-binding cassette protein were upregulated, while peptidoglycan recognition protein was downregulated. Innate immune response genes, such as glutathione S-transferases, acetylcholinesterase, and heat shock protein, were also identified in the transcriptome. The findings signify that changes occurred in the insect’s receptor and signaling, metabolic detoxification of insecticides, and immune responses upon FA treatment on M. plana. This valuable information on FA toxicity may be used to formulate more effective biorational insecticides for better M. plana pest management strategies in oil palm plantations.

In silico prediction of effector proteins in oil palm pest, Metisa plana

Metisa plana is one of the main oil palm leaf defoliators in Malaysia. Infestation of M. plana at the oil palm plantation caused severe yield loss in the past. Many control methods have been introduced to combat the infestation, but the problem persists till today. More information regarding the bagworm is needed to understand the pest-host interaction so that an effective control method can be properly designed. This study was carried out to predict the repertoire of effector proteins secreted by M. plana that could potentially suppress the host defence mechanisms. In silico screening of the secretory proteins from predicted genes was done by using several tools namely SignalP, TMHMM, TargetP and WOLFPSORT. Then, the acquired dataset was subjected to functional annotation through a similarity search against UniProt-Trembl database, gene ontology (GO) using BLAST2GO, InterPro protein domain classification, cluster of orthologous groups (COG), and KEGG pathway prediction. To determine the expression of the predicted genes, TPM value was calculated using the available transcriptomic dataset of M. plana. We identified a total of 948 secretory proteins from the initial screening of the 28,738 proteins predicted from BRAKER2 pipeline. Several proteins were classified as specific protein classes associated with effector functions in insect pests, namely detoxifying enzymes, lipases, peroxidases, Ca2+ binding proteins, and chemosensory and odorant binding proteins. This predicted effector data can be used as a reference for future works in validating the protein’s interaction and mechanisms with the oil palm defence responses.

Population and attack of bagworm pests in smallholder oil palm plantations in Tenggulun Sub District, Aceh Tamiang District, Aceh

Bagworms are leaf-eating insects that are quite greedy and difficult to control because these pests hide in their bags. This pest control needs to be done especially on immature plants. This research aims to determine the dynamics of bagworm populations in immature oil palm plants and the percentage of bagworm attacks in smallholder oil palm plantations with different management. This research was carried out by direct observation of bagworms on Smallholder Oil Palm Plantations in Selamat Village and Tenggulun Village. In each village, 2 observation locations were determined and 25 plant samples were selected from each observation location. Dead bagworms are collected to determine the cause of death. The results of the research found 3 species of bagworms at the research location (Mahasena corbetti, Metisa plana and Cremastopsyche pendula). The most commonly found bagworm species is Mahesa plana. Mahasena corbetti is a priority to control because its attack level is in the medium category. Furthermore, the percentage of bagworm attacks ranges from 38-76%. The bagworm population in Selamat Village is lower than in Tenggulun Village, this is because oil palm farmers in Selamat Village have implemented integrated pest control methods in managing their oil palm plants, (utilizing natural enemies/predators, manual eradication of bagworm pests and trunk injection.

Comparison of different variable selection methods for predicting the occurrence of Metisa Plana in oil palm plantation using machine learning

Monitoring and predicting the spatio-temporal distribution of crop pests and assessing related risks are crucial for effective pest management strategies. Machine learning techniques have shown potential in analysing agricultural data and providing accurate predictions. Variable selection plays a critical role in crop pest analysis by identifying the most informative and influential features that contribute to pest distribution and risk prediction. The current practice of choosing variable selection methods is mostly based on previous experience and may involve a certain degree of subjectivity. This paper aims to provide empirical comparisons of different variable selection methods for machine learning applications in crop pest spatio-temporal distribution and risk prediction. This study conducted various variable selection methods, including filter methods (information gain, chi-square test, mutual information), wrapper methods (RFE), and embedded methods (Random Forest), using worms pest (Metisa plana) in oil palm trees as the experimental subject. The initial set of variables included bioclimatic, vegetation indices, and terrain variables. The experimental results indicated that there was some overlap in the selected variables across different methods, bioclimatic variables (rainfall (RF), relative humidity (RH)) were selected as important variables by different methods; non-important variables like NDVI and elevation when added to the ANN modelling can clearly contribute to the improvement in prediction accuracy. These empirical findings can provide guidance for relevant data monitoring in the prediction of crop pest and disease outbreaks. Additionally, the results can serve as a reference for variable selection in spatiotemporal prediction of pests and diseases in other agricultural and forestry crops.

EFFECTIVENESS OF DRONE SPRAYING TO CONTROL BAGWORM OUTBREAK

Outbreaks of the bagworm Metisa plana often occur in Malaysian oil palm plantations. Given the complexities of the M. plana lifecycle, the high cost of lower-toxicity chemical pesticides, and the labour shortage, the application of unmanned aerial vehicles (UAVs), also known as drones, was used, as it is more practical at a commercial scale. In July 2022, FGVPM Besout 6 and Besout 7, with a total planting area of 2 110.20 and 2 737.30 ha, respectively, experienced massive M. plana infestations with a total outbreak area of 622.50 ha (29.50%) and 650.00 ha (23.75%), respectively. Within five days, drones equipped with fan nozzles for spraying flubendiamide insecticide at a rate of 36 L per hectare were able to combat the infestation. Water-sensitive paper was set up to ensure the droplet distribution met the standard operating procedure requirement. ImageJ analysis confirmed the sprayed coverage area was excellent, especially on the upper frond. At 30 days after spraying (DAS), the number of live M. plana individuals per frond dropped to zero compared to the non-treated area, which counted 82.50 M. plana. The effectiveness of spraying is supported by its high productivity as compared to the conventional approach of trunk injection. The results demonstrated that with the right dosage of pesticides, drone spraying with proper specification is more feasible, affordable, saves time, and has been proven to be efficient for M. plana treatment on a commercial scale. Keywords: Unmanned aerial vehicle, drone, oil palm, precision agriculture, bagworm Metisa plana.

Detection of Bagworm Infestation Area in Oil Palm Plantation Based on UAV Remote Sensing Using Machine Learning Approach

Due to its rapid reproduction rate and brief life cycle, the most well-known oil palm pest, Metisa plana (Lepidoptera: Psychidae), also known as the bagworm, can spread to epidemic proportions. The outbreak can significantly reduce oil palm yield by resulting in 40% crop losses and 10% to 13% leaf defoliation. A manual census was conducted to count the number of pests and determine the category of infestation; however, when covering a large area, it typically takes more time and labour. Therefore, this study used unmanned aerial vehicles (UAVs) as a quick way to detect the severity levels of infestation in oil palm plantations, including healthy (zero), low, mild, and severe infestation using DJI Inspire 2 with Micasense Altum-PT multispectral camera at an altitude of 70 m above ground. Three combinations were created from the most significant vegetation indices: NDVI and NDRE, NDVI and GNDVI, and NDRE and GNDVI. According to the results, the best combination in classifying healthy and low levels was found to be NDVI and GNDVI, with 100% F1 score. In addition, the combination of NDVI and NDRE was found to be the best combination in classifying mild and severe level. The most important vegetation index that could detect every level of infestation was NDVI. Furthermore, Weighted KNN become the best model that constantly gave the best performance in classifying all the infestation levels (F1 score > 99.70%) in all combinations. The suggested technique is crucial for the early phase of severity-level detection and saves time on the preparation and operation of the control measure.

Gene Co-Expression Network Analysis Reveals Key Regulatory Genes in Metisa plana Hormone Pathways.

Metisa plana Walker (Lepidoptera: Psychidae) is a major oil palm pest species distributed across Southeast Asia. M. plana outbreaks are regarded as serious ongoing threats to the oil palm industry due to their ability to significantly reduce fruit yield and subsequent productivity. Currently, conventional pesticide overuses may harm non-target organisms and severely pollute the environment. This study aims to identify key regulatory genes involved in hormone pathways during the third instar larvae stage of M. plana gene co-expression network analysis. A weighted gene co-expression network analysis (WGCNA) was conducted on the M. plana transcriptomes to construct a gene co-expression network. The transcriptome datasets were obtained from different development stages of M. plana, i.e., egg, third instar larvae, pupa, and adult. The network was clustered using the DPClusO algorithm and validated using Fisher's exact test and receiver operating characteristic (ROC) analysis. The clustering analysis was performed on the network and 20 potential regulatory genes (such as MTA1-like, Nub, Grn, and Usp) were identified from ten top-most significant clusters. Pathway enrichment analysis was performed to identify hormone signalling pathways and these pathways were identified, i.e., hormone-mediated signalling, steroid hormone-mediated signalling, and intracellular steroid hormone receptor signalling as well as six regulatory genes Hnf4, Hr4, MED14, Usp, Tai, and Trr. These key regulatory genes have a potential as important targets in future upstream applications and validation studies in the development of biorational pesticides against M. plana and the RNA interference (RNAi) gene silencing method.

Effectiveness of Cordyceps fumosorosea Wettable Powder Formulation against Metisa plana (Walker) and Its Side Effects on Elaeidobius kamerunicus in Oil palm Plantation.

Development of mycoinsecticides with Cordyceps fumosorosea as an active ingredient is established as an alternate way to control the Metisa plana population while reducing chemical insecticide dependence. Three mycoinsecticide formulations (SS6, SS7, and SS8) with dispersing and wetting agents were developed as wettable powder formulations in this trial. SS8 demonstrated the best wettability, suspensibility, and dispersibility with viability at 107 (CFU)/mL even after three months of storage. However, SS7 developed with C. fumosorosea as an active ingredient was found to effectively reduce the bagworm population by more than 95%. The application of all mycoinsecticide formulations in the infested oil palm area was able to reduce the M. plana population by more than 95%, 30 DAT. The formulations also show no significant increase in mortality of the oil palm pollinator, Elaeidobius kamerunicus. This finding indicates that the C. fumosorosea tested has potential for managing bagworms without harming pollinators on oil palm plantations.

Uncovering the Microbiota of Bagworm Metisa plana (Lepidoptera: Psychidae) in Oil Palm Plantations in Malaysia.

Bagworm Metisa plana is one of the major pests in Malaysia's oil palm plantation, with infestation resulting in huge economical loss. Currently, the microbial profile of the bagworm has yet to be study. Understanding the biology of the pest such as the bacterial community is crucial as bacteria associated with insects often provide benefits to the insect, giving the insect host a better chance of survival. Here, 16S amplicon sequencing was used to identify the bacteria community of M. plana. Additionally, two comparisons were made, the bacterial communities between two larval stages (early instar stage and late instar stage) from outbreak area; the bacterial communities of late instar stage larvae from non-outbreak between outbreak areas. From this study, it was found that the bacterial community of M. plana consisted of Proteobacteria, Actinobacteria, Bacterioidetes, Firmicutes and other minor phyla, with Proteobacteria being the most dominant phylum. Furthermore, bacterial genera of M. plana consisted of Pantoea, Curtobacterium, Pseudomonas, Massilia and other minor genera, with Pantoea being the most dominant. It was also found that the alpha and beta diversity in both comparisons were not significantly different. We present our data as a first insight towards the bacterial community of M. plana, paving a way towards understanding the biology of the bagworm M. plana.

FEEDING PREFERENCE OF BAGWORM, METISA PLANA WALKER (LEPIDOPTERA: PSYCHIDAE) ON FOUR DIFFERENT PALM SPECIES

A study was conducted to determine the feeding preference of the bagworm, Metisa plana on different palm species in no-choice and choice feeding tests. In the laboratory study, the larvae of bagworm, M. plana were fed with leaves of four palm species namely oil palm (Elaeis guineensis), coconut (Cocos nucifera), red sealing wax palm (Cyrtostachys renda) and salak (Salacca zalacca). In the no-choice feeding test, the bagworms were fed with leaves of each selected palm species and in the choice feeding test, the M. plana were fed in pairs with leaves of E. guineensis as a constant diet and each of C. nucifera, C. renda and S. zalacca respectively. Furthermore, the feeding preference of bagworms on E. guineensis and C. nucifera leaves was evaluated in a semi-field trial. The result of the laboratory study showed that C. nucifera was the most preferred host plant followed by E. guineensis, C. renda and S. zalacca. The M. plana fed with C. nucifera leaves recorded 28.83 cm2 and 22.01cm2 of total leaflet area consumed in the no-choice feeding test and choice feeding test respectively. The bagworms feeding on leaves of C. nucifera showed 33.33 per cent survival rate and were observed to have the fastest larval development and transformation to pupal stage. The leaves of C. nucifera were preferred over E. guineensis despite the latter being the observed host plant of this bagworm species. In no-choice assessments, the M. plana fed with E. guineensis leaves recorded a total leaf area consumption at 26.89 cm2 and showed only a 13.33 per cent survival rate. C. renda and S. zalacca leaves were recorded as the least preferred host plant and recorded a low leaf area consumption at 14.67cm2 and 11.40 cm2 in no-choice feeding and 1.29 cm2 and 5.05 cm2 in choice feeding respectively. The M. plana that were fed with the two respective palms did not survive to the pupal stage. In the semi-field study, similar results were obtained as C. nucifera was recorded as the more preferred host plant by the bagworm as compared to E. guineensis. The cumulative damage on the leaves of the palms was recorded at 29.37 per cent for C. nucifera and 13.13 per cent for E. guineensis. In the choice feeding test, the bagworms fed with leaves of C. nucifera recorded 12.71 per cent and that of E. guineensis at 8.19 per cent of cumulative damage. The percentage of survival rate of bagworms that were fed with C. nucifera leaves was recorded to be significantly higher (P<0.05) at 18.75 ± 2.16 per cent as compared to E. guineensis that recorded only 9.50 ± 1.53 per cent. Keywords: Bagworm, Metisa plana, feeding preference, oil palm, coconut, red sealing wax palm, salak palm.

De novo transcriptome analysis of bagworm Metisa plana from highly infested oil palm estate in Perak revealed detoxification genes and potential insecticide targets

De novo transcriptome analysis of bagworm Metisa plana from highly infested oil palm estate in Perak revealed detoxification genes and potential insecticide targets

Automatic Classification of Bagworm, Metisa plana (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.

Ensemble-based, high-throughput virtual screening of potential inhibitor targeting putative farnesol dehydrogenase of Metisa plana (Lepidoptera: Psychidae)

Metisa plana (Lepidoptera: Psychidae) bagworm is a leaf-eater caterpillar ubiquitously found as a damaging pest in oil palm plantations, specifically in Malaysia. Various strategies have been implemented, including the usage of chemical insecticides. However, the main challenges include the development of insecticide resistance and its detrimental effects on the environment and non-target organisms. Therefore, a biorational insecticide is introduced by targeting the juvenile hormone (JH) biosynthetic pathway, which is mainly present in the insect and vital for the insect's growth, diapause, metamorphosis, and adult reproduction. This study aimed to investigate the potential inhibitor for the rate-limiting enzyme involved in the JH pathway known as farnesol dehydrogenase. A 255 amino acids sequence encoded for the putative M. plana farnesol dehydrogenase (MpFolDH) open reading frame had been identified and isolated. The three-dimensional structure of MpFolDH was predicted to have seven β- sheets with α-helices at both sides, showing typical characteristics for classical short-chain dehydrogenase and associated with oxidoreductase activity. Then, the ensemble-based virtual screening was conducted based on the ZINC20 database, in which 43 768 compounds that fulfilled pesticide-likeness criteria were screened by site-specific molecular docking. After a short molecular dynamics simulation (5 ns) was conducted towards 102 compounds, only the top 10 compounds based on their most favourable binding energy were selected for a more extended simulation (100 ns). Based on the protein-ligand stability, protein compactness, residues rigidity, binding interaction, binding energy throughout the 100 ns simulation, and physicochemical analysis, ZINC000408743205 was selected as a potential inhibitor for this enzyme. Amino acids decomposition analysis indicates Ile18, Ala95, Val198 and Val202 were the critical contributor residues for MpFolDH-inhibitors(s) complex.

Foraging Behaviour and Population Dynamics of Asian Weaver Ants: Assessing Its Potential as Biological Control Agent of the Invasive Bagworms Metisa plana (Lepidoptera: Psychidae) in Oil Palm Plantations

The bagworm (Metisa plana) is a recurrent indigenous invasive defoliator in oil palm plantations. Moderate foliar injury can cost up to 40% yield loss and more for years. The main objective of this review is to disseminate published research demonstrating the versatile services that would benefit farmers by adopting the Asian weaver ant into their pest management agenda. Oecophylla smaragdina is a natural indigenous enemy applied as a successful biological control agent (BCA) and strong component of integrated pest management (IPM) against important damaging pest infestations of commercial crops in the Asia-Pacific region. Farmers facing invasion could benefit by introducing Oecophylla ants as a treatment. The foraging behavior and population dynamics of this species are poorly documented, and hence need further evaluation. Ants of the Oecophylla genus, while exhibiting an intrinsic obligate arboreal pattern, demonstrate additional lengthy diurnal ground activity. The absolute territorial characteristic via continuous surveillance is significantly valuable to maintain pest balance. The exploratory scheme of major workers over large territories is derived from their inner predation instinct. The insufficient understanding of the population dynamics of this weaver ant species diverges from the knowledge of underground species. However, population density estimations of weaver ants by direct nest visual recordings are practicable and viable. The abundance assessment of individual underground ant species colonies by excavation ends with their extinction, which is not a sustainable model for O. smaragdina. Mathematical model estimation by simulation could not resolve this issue, adding inaccuracy to the deficiency of experimental proof. Thus, long-term monitoring of the population dynamics in real time in the field is compulsory to obtain a valid dataset. Oecophylla colonies, with the criteria of population stability, individual profusion, and permanent daily patrol services, are eligible as a BCA and alternative IPM treatment. The last decades have witnessed the closing of the scientific applied research gap between Asian and African species in favor of O. longinoda with comprehensive novel findings. By introducing Oecophylla ants, two main goals are reached: easing the burden of management costs for injurious insects and ending the practice of applying highly toxic pesticides that are harmful to non-target taxa, thus promoting environmental restoration.

Ground based thermographic screening of bagworm (<em>Metisa plana</em> Walker) infestation in oil palm and identification of their growth stages

Bagworms (Lepidoptera: Psychidae) are one of the main species of vicious leaf-eating insect pests that affects oil palm plantations in Malaysia. A moderate bagworm attack of 10-50% leaf damage may cause 43% yield loss. In 2020, the economic loss due to the bagworm attack in Malaysia is estimated as RM 180 million. Hence, it is necessary to monitor closely the bagworm outbreak in infested areas. However, precise data collection with accuracy is a challenging task. In this context, we explored the possibility of using thermal infrared (TIR) cameras (T 440) for detection of bagworms and identification of bagworm growth stages at the infested areas for effective monitoring. The reflector method was applied to detect the reflected apparent temperature and emissivity of the bagworms using thermographic measurement techniques. The results have revealed that the bagworms from the 1st to 7th larval instar and pupal stages exhibit emissivity values ranging from 0.88 to 0.89. Two rounds of our observation have shown that the bagworms can be detected during evening and afternoon vision as compared to night, midnight and morning vision, with consideration of emissivity, solar radiation, and snapshot distance at a percentage accuracy of 74 and 85%, respectively. The classification of the larval and pupal stages was carried out by grouping the larval and pupal stages based on their real size; Group I: larval stage 1-3, Group II: larval stage 4-7; and Group III: pupal stage. Identification of the bagworm stages according to the classified group and the object led to differentiation between the larval stages from Gr. I, II, and III, with the per cent detection of 60, 84 and 89%, respectively. The results indicate that the thermographic imaging approach can be applied to detect bagworms, provided all conditions and time observation are taken into consideration. This thermographic technique deserves thorough testing and optimization for detection of bagworms in oil palm plantations.

The impact of bagworm (Metisa plana) attack on the production of oil palm (Elaeis guineensis Jacq.) at Tanjung Beringin Plantation PT. Langkat Nusantara Kepong

The impact of bagworm (Metisa plana) attack on the production of oil palm (Elaeis guineensis Jacq.) at Tanjung Beringin Plantation PT. Langkat Nusantara Kepong. This study aims to assess the extent of attack by bagworms (Metisa plana) and the impact of bagworm attacks (Metisa plana) on oil palm production in Tanjung Beringin Plantation PT. Langkat Nusantara Kepong. The study method used was descriptive statistics by collecting data from the estate office and direct interviews. The study was carried out at Afdeling III Tanjung Beringin Plantation, PT. Langkat Nusantara Kepong. The assessment was carried out from February to July 2022. The statistical analysis method used to determine the impact of bagworm (Metisa plana) attacks on oil palm production was using tables, graphs and Descriptive Statistics analysis. The results of tables, graphs and Descriptive Statistics analysis show that the attack area of the bagworm (Metisa plana) in 2019-2021 has decreased by 66.62% and the attack of the bagworm (Metisa plana) has an impact on oil palm production. The wider the pest attack, the smaller the production. Pest attacks in 2019 of 680 Ha experienced a decline in production in 2020 by 2.33% and pest attacks in 2020 of 374 Ha experienced an increase in production in 2021 by 11.60%.

The Attack Rate of Pocket Caterpillar (Metisa plana) And How To Control It on Oil Palm (Elaeis Guineensis Jacq) Plantation At PT Umada Pernantian “A”, North of Labuhanbatu Regency

Oil palm (Elaeis guineensis jacq.), is a vegetable oil-producing plant, which cannot be separated from the attack of caterpillars that eat oil palm leaves which can cause a decrease in plant production. The purpose of this study was to determine the attack rate of bagworm (Metisa plana) and how to control it, on each oil palm plant block at PT. Umada Pernantian "A", Kec. Marbau, North Labuhan Batu Regency. The method used in this research is descriptive method by collecting primary data and secondary data. Primary data is the result of observations in the field with a pest census using a descriptive method. Secondary data is data from the previous pest census in the company. The highest attack rate of Metisa plana was found in block I and block II with an incidence of 18% in block I and 8% in block II, the number of pests in block I was 27,336, and 18,480 in block II, which was >5 tails / midrib with weight category. The lowest Metisa plana attack rate was found in block III and block V where the percentage of bagworm attacks in block III: 0.08% and in block V: 0.11%, the number of pests in block III: 23 tails and block V: 26 tails, with an attack rate of <2 bagworms per midrib. After chemical control was carried out in blocks I and II, the percentage of attacks in block I: 0.43% and block II: 0.34%, the number of pests in block I: 100 and 70 in block II, the attack rate in block I and II were being <2 tails/midrib in the light category

Interactions between Metisa plana, its hyperparasitoids and primary parasitoids from good agriculture practices (GAP) and non-gap oil palm plantations

Interactions between Metisa plana, its hyperparasitoids and primary parasitoids from good agriculture practices (GAP) and non-gap oil palm plantations

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

• Young larvae can be detected using hyperspectral imaging and machine learning. • The best model was obtained using green bands and weighted k-nearest neighbour. • Larvae activities gave a significant impact on the selection of the best dataset. • Larvae size is nearly identical, therefore morphological features underperform. 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.