Silent Invaders: Mapping the pest peril in warehouses storing medicinal products

S Paripoorani; M Shanthi; M Murugan; N Senthil; N Vijaykumari; S Haripriya; M Jayakanthan

doi:10.14719/pst.7777

Authors

S Paripoorani Department of Agricultural Entomology, AC & RI, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0009-0001-5610-5034
M Shanthi Directorate of Centre for Plant Protection Studies, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0001-5737-4781
M Murugan Department of Agricultural Entomology, AC & RI, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0002-7485-1153
N Senthil Directorate of Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0002-2930-0715
N Vijayakumari Department of Agricultural Entomology, AC & RI, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0009-0002-6300-9558
Haripriya S Department of Vegetable Science, HC & RI, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0003-0541-4221
M Jayakanthan Department of Plant Molecular Biology and Bioinformatics, AC & RI, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0001-7444-9343

DOI:

https://doi.org/10.14719/pst.7777

Keywords:

cluster analysis, medicinal crop products, pest management, principal component analysis, storage pest, survey

Abstract

A structured survey-based study was conducted to assess storage conditions, pest infestations and management strategies in medicinal crop produce storage warehouses. Data were collected from ten warehouse locations across Tamil Nadu through structured questionnaires and direct observations. The warehouses, specializing in storing medicinal plant parts such as seeds, roots, leaves and bark, were analyzed based on storage capacity, duration and environmental conditions, including temperature and humidity levels. Pest infestations were documented by identifying species diversity, infestation rates and seasonal trends. Infestation levels were categorized as low, moderate, or high, with data on estimated storage losses. Pest control measures were assessed, including preventive approaches like sanitization, ventilation and humidity control, as well as curative strategies using synthetic insecticides, fumigants and botanical-based pest management solutions. The collected data were pre-processed in MATLAB, utilizing Principal Component Analysis (PCA) and K-means clustering to identify significant infestation patterns. PCA results indicated that the first two principal components captured most of the variance in pest associations, with certain crops such as Curcuma longa (turmeric) and Zingiber officinale (ginger) showing high susceptibility to Lasioderma serricorne, whereas Myristica fragrans (nutmeg) exhibited infestations from multiple pests, including Sitophilus oryzae and Oryzaephilus sp. K-means clustering identified distinct groupings of medicinal crops based on pest infestations, highlighting the need for tailored pest management strategies. A heat map analysis further confirmed that L. serricorne and Oryzaephilus sp. posed the greatest threats to specific medicinal crops, emphasizing the importance of targeted pest mitigation efforts. The hierarchical clustering of medicinal crops suggested shared susceptibility patterns, reinforcing the necessity for customized interventions. These findings provide crucial insights for optimizing storage conditions, improving pest management practices and reducing post-harvest losses in medicinal crop warehouses.

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