Outbreak of invasive thrips species Thrips parvispinus in chilli growing areas of Andhra Pradesh

Sireesha, K.; B.V.L., Prasanna; T., Vijaya Lakshmi; R.V.S.K., Reddy

doi:10.55278/jyzr1153

Cited by 2 publications

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“…Many of these illnesses, including Magnaporthe oryzae (Germany), Botrytis cinerea, Puccinia spp., Fusarium graminearum, Fusarium oxysporum, Black Thrips (India), Blumeria graminis, Mycosphaerella graminicola, Colletotrichum spp., Ustilago maydis, and Melampsora lini, have been identified by researchers and practitioners in recent times (Ceballos et al, 2020;Singh et al, 2023;Chen et al, 2023;Carreón-Anguiano et al, 2020). The states of India such as Telangana, Karnataka, and Andhra Pradesh saw significant crop loss because of the new invasive thrips species, Thrips parvispinus, particularly the "Black Thrips" on chillies (Lodaya et al, 2022;Sireesha et al, 2021). Due to Thrips attack on Chilli crop, approximate 191.17 m.t crop loss is observed in India during 2021-22 (Figure 1).…”

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confidence: 99%

Deep Learning Methods for Detecting Chilli Pests: A Novel Performance Analysis

Kanaparthi,

Ilango

2024

Ecol. Eng. Environ. Technol.

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Ensuring food security is a top goal for all nations, yet infected plants can negatively impact agricultural production and the country's economic resources. In the past, farmers have depended on conventional techniques to enhance crop yield. In recent times, there has been a significant decline in crop production due to pest infestations on Chilli crops. The progress of deep learning techniques facilitates the categorization of diverse sorts of images in practical applications. Especially, detecting multi-class Chilli crop pests with good accuracy using deep learning algorithms is consistently a significant challenge. The proposed study concentrated in identifying pests on Chilli leaves using deep learning methods such as YOLOv5 and YOLOv7. To improve classification accuracy, a new and unique dataset called the standard balanced custom 'Chilli pest dataset' is created with 13,414 pest images. This dataset includes three specific pest classes: Black Thrips, Redmites, and White Fly. We analysed the custom Chilli dataset using YOLOv5 and YOLOv7 to evaluate their effectiveness in detecting pests in Chilli crops and obtained novel detection performance metrics. The resultant parameters mean Average Precision (mAP) for all three pest classes is 98.6% for YOLOv5 and 86.1% for YOLOv7. The YOLOv5s detector demonstrates superior performance compared to the YOLOv7 pest classification, with a 12.5% improvement. The YOLOv7 algorithm achieves its best classification accuracy (86.1%) at a lower epoch (110), while the YOLOv5 algorithm achieves its highest classification accuracy (98.6%) at a higher epoch (350). Nevertheless, despite this distinction, the YOLOv5 algorithm is recommended as the superior detector for accurately identifying pests in well-balanced multi-class pest type datasets, in comparison to YOLOv7, VGG-16 (~92.7%), and VGG-19 (~84.24%) deep learning architectures.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Methods for Detecting Chilli Pests: A Novel Performance Analysis

Kanaparthi,

Ilango

2024

Ecol. Eng. Environ. Technol.

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Field efficacy of insecticides against chilli thrips (Scirtothrips dorsalis) and their effect on coccinellids

et al. 2022

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Considering the economic significance of S. dorsalis in chilli crop, a study was carried out at the ICAR-Research Complex for Eastern Region, Farming System Research Centre for Hill and Plateau Region, Ranchi, Jharkhand during the spring season of 2019 and 2020 to find out the effective chemical molecules for managing this pest. Effectiveness of 8 modern insecticides against S. dorsalis and their effect on coccinellid predators was evaluated. Efficacy of insecticides was determined by comparing number of thrips, immature stages and adults of natural enemies, marketable yield in insecticide treated versus untreated control plots. Application of spinosad @70 g a.i./ha, Emamectin benzoate 5 sg @11 g a.i./ha, Imidacloprid 200 sl @40 g a.i./ha and Fipronil 5 sc @30 g a.i./ha were found to be effective against S. dorsalis in chilli. Other insecticides were inconsistent in effectiveness against S. dorsalis population. Fenpropathrin followed by Imidacloprid were found to cause maximum reduction and Spinosad and Emamectin benzoate caused lowest reduction of coccinellid population. Thus, based on the present study, Spinosad, Emamectin benzoate, Fipronil and Imidacloprid are recommended to manage S. dorsalis on rotational basis in chilli ecosystem.

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Outbreak of invasive thrips species Thrips parvispinus in chilli growing areas of Andhra Pradesh

Abstract: In my last editorial I wrote that it was a 'floody' quarter. Well, this quarter, till early December, as I write this,

Cited by 2 publications

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Deep Learning Methods for Detecting Chilli Pests: A Novel Performance Analysis

Deep Learning Methods for Detecting Chilli Pests: A Novel Performance Analysis

Field efficacy of insecticides against chilli thrips (Scirtothrips dorsalis) and their effect on coccinellids

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