Seasonal patterns of insect pest in major pigeonpea and chickpea growing agro-climatic zones of India and their management inferences

Bapatla, Kiran Gandhi; Singh, S. R.; Vennila, S.; Singh, B. B.; Singh, N. P.

doi:10.1007/s42690-020-00361-y

Cited by 3 publications

(10 citation statements)

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“…Although geostatic tools have been utilized to understand spatiotemporal distribution in H. armigera , 16 Hypera postica 15 and Anastrepha ludens 42 in tomato, alfalfa and citrus, respectively, this study is the first of its kind to document the spatial distribution of H. armigera in pigeonpea. Our results were further supported by earlier studies of H. armigera temporal variation in pigeonpea 9–11 …”

Section: Discussionsupporting

confidence: 91%

“…A significant H. armigera larval population was recorded in pigeonpea compared with cotton in India 40 ; therefore, we predicted H. armigera larval movement from cotton (non‐Bt) to pigeonpea crop during the pod formation phenophase of pigeonpea (44 MSW). Bapatla 9 stated that staggered sowing dates and different plant varieties influenced the in‐crop recruitment of H. armigera in pigeonpea; similarly, in our experimental area, different planting dates (from June to August) and pigeonpea varieties (LRG‐41, ICPL 87119, ICPL 85063, WRG‐97 and WRG‐65) might have significantly affected within‐season H. armigera larvae incidence.…”

Section: Discussionsupporting

confidence: 55%

“…Likewise, Moral Garcia 16 reported that maximum densities of H. armigera larvae were first noticed at the borders of tomato fields (experimental area) and later progressed towards the centre of the field. Meteorological conditions, 16 host availability, biotic factors (parasitoids and predators) and increased survival strategies 9 may affect the expansion and colonization of H. armigera in unsampled locations of an experimental area (Figure 5).…”

Section: Discussionmentioning

confidence: 99%

“…In the experimental area, the association of pigeonpea plant phenology and H. armigera larval incidence was interpreted by recording different pigeonpea phenophases corresponding to MSWs. In this study, one larva per plant was taken as the economic threshold level (ETL) for H. armigera in pigeonpea 9 …”

Section: Methodsmentioning

confidence: 99%

“…reported seasonal patterns in insect pests including identification of season maxima and their temporal dynamics over the cropping season, and provided figures to develop a management initiative. Spatial distribution of H. armigera in pigeonpea crop was not characterized, although earlier studies have demonstrated seasonal population dynamics in this pest 9–11 . Understanding the spatial distribution patterns of an insect pest is useful in the development of sampling plans by identifying the minimum intersample distance needed to obtain independent samples, 12–14 and is critical in site‐specific management of an insect pest 15 …”

Section: Introductionmentioning

confidence: 99%

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Characterization of Helicoverpa armigera spatial distribution in pigeonpea crop using geostatistical methods

Seethalam

Bapatla

Kumar

et al. 2021

Pest Management Science

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BACKGROUND The gram pod borer, Helicoverpa armigera (Lepidoptera: Noctuidae) is an economically important pest of pigeonpea crop in India. Fixed plot surveys for H. armigera larvae were carried out at 28 pigeonpea fields located in the Southern Plateau and Hills agro‐climatic zone of India for three crop seasons (nine sampling weeks per season). The spatiotemporal dynamics of H. armigera larvae in the experimental area (=Hanamkonda) was analysed using geostatistics tools, namely a variogram and Voronoi diagram, and H. armigera larval distribution patterns were further characterized and mapped. RESULTS A significant difference in H. armigera larval incidence was noticed between sampling weeks, with greater larval incidence observed between 26 November and 2 December. Pod formation phenophase (Meteorological Standard Week 44) of pigeonpea favoured the initial H. armigera larval incidence. Variogram analysis revealed moderate to strong larval aggregation (spatial dependence) of H. armigera in all nine sampling weeks. Based on the range value of the variogram, the average aggregation distance of H. armigera larvae in pigeonpea was estimated to be 2425.48 m. Voronoi diagrams illustrated the spatial heterogeneity of H. armigera larva between sampling weeks, which can be linked to availability of food sources. CONCLUSION This study witnessed intrapopulation variability in H. armigera larvae associated with geographical space and temporal patterns. Based on our findings, a sampling distance of 2425.48 m may be used in larger pigeonpea fields (experimental area) to reduce scouting fatigue. The interpolated maps generated in this study may be of value in developing effective H. armigera larva monitoring and management tools in pigeonpea crop.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterization of Helicoverpa armigera spatial distribution in pigeonpea crop using geostatistical methods

Seethalam

Bapatla

Kumar

et al. 2021

Pest Management Science

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Climate Change Poses Threat to Helicoverpa zea Boddie (Lepidoptera: Noctuidae)

Saranya,

Senthilkumar,

Yuvaraj

et al. 2024

Plant Quarantine Challenges Under Climate Change Anxiety

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Major nocturnal Pest classification model using Faster RCNN architecture of Deep learning

J. Patel,

Bhatt

2021

IJSRST

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Agriculture research improves the quality and quantity of crops, but pests degrade them. Pesticides are used to prevent these pests from reproducing. However, excessive pesticide use is extremely detrimental to both production and the environment. As a result, initial pest detection is required. We analyzed the most frequently used methodologies in order to determine the most appropriate technique for the first diagnosis and early detection of significant nocturnal flying pests such as White Grub, Helicoverpa, and Spodoptera. We identified and analyzed three frequently used deep learning meta-architectures (Faster R-CNN, SSD Inception, and SSD Mobilenet) for object detection using a small Pest dataset. The faster RCNN meta-architecture outperforms other meta-architectures. To address the issue of class imbalance, we used image augmentation with a Faster RCNN meta-architecture. The proposed work demonstrates how to classify Nocturnal Pests using a Faster RCNN of Deep Learning with a better accuracy performance on a limited dataset and utilization as decision-making tool based on classified results.

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Seasonal patterns of insect pest in major pigeonpea and chickpea growing agro-climatic zones of India and their management inferences

Cited by 3 publications

References 10 publications

Characterization of Helicoverpa armigera spatial distribution in pigeonpea crop using geostatistical methods

Characterization of Helicoverpa armigera spatial distribution in pigeonpea crop using geostatistical methods

Climate Change Poses Threat to Helicoverpa zea Boddie (Lepidoptera: Noctuidae)

Major nocturnal Pest classification model using Faster RCNN architecture of Deep learning

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