Use of a geographic information system to produce pest monitoring maps for south Texas cotton and sorghum land managers

Deleon, Leonel; Brewer, Michael J.; Esquivel, Isaac L.; Halcomb, J. L.

doi:10.1016/j.cropro.2017.07.016

Cited by 16 publications

(3 citation statements)

References 15 publications

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“…Penggunaan cascaded convolutional neural networks (CNN) menghasilkan akurasi pencacahan 88-95%, sedangkan pengklasifikasi serangga multi-class memiliki akurasi 86 -92% dengan waktu pemrosesan rata-rata 13-15 detik dan 2-3 detik [19]. Penggunaan teknologi GIS juga dapat mengurangi perkembangan serangga hama [20].…”

Section: Detektor Elektronikunclassified

Kajian Algoritma Deteksi Hama Gudang Menggunakan Metode Pengukuran Intensitas Pantulan Cahaya Objek

Hudaya

Budiawan²,

Sutian³

et al. 2021

IRWNS

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Section: Detektor Elektronikunclassified

Kajian Algoritma Deteksi Hama Gudang Menggunakan Metode Pengukuran Intensitas Pantulan Cahaya Objek

Hudaya

Budiawan²,

Sutian³

et al. 2021

IRWNS

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“…Generally, the implementation of an integrated pest management program entails: (1) sampling and monitoring; (2) determination of the impact on the crop in terms of injury, damage, and loss; (3) application of treatments and evaluation of management practices; (4) forecasts of future population dynamics; and (5) implementation of site-specific agriculture (Brenner et al 1998;Arbogast et al 2000;Binns et al 2000;Ferguson et al 2003). Monitoring and subsequent spatial analysis are key to generating basic information on population dynamics, species detection, behavior, and pest distribution (Binns et al 2000;Deleon et al 2017;Arias et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Population Dynamics and Estimation of Damage of the Spittlebug Aeneolamia varia on Sugarcane in Colombia by Using remote Sensing and Machine Learning Tools

et al. 2023

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The spittlebug (Aeneolamia varia) is one of the most important sugarcane pests in Colombia, where a recent increase in population and distribution specially in southwestern Colombia have led to the need for new technologies for integrated pest management. The objectives of this study were to determine the spatial distribution of this pest in commercial sugarcane fields and to validate machine learning (ML) tools for indirect injury detection and impact on yield (damage) using satellite images. This study was carried out in fields grown with the CC 01-1940 variety in El Cerrito, Valle del Cauca, Colombia, where systematic sampling of the populations (number of adults and nymphs per stem) was carried out. The spatial aggregation and distribution were determined using Moran’s index and point patterns, sequence observations, and analysis with distance indicators (Sadie). The indirect injury detection and quantification of the impact on production were carried out with a ML approach using satellite image products with 10 m spatial and five days temporal resolutions, obtained from a Sentinel-2 sensor using Google Earth Engine. The results indicated that spittlebug populations had an aggregate spatial behavior and high spatial dependence. In addition, the ML algorithms predicted spittlebug injury, and the effect on production was estimated at 26.4 tons of cane per hectare, which represented a 17% reduction in the expected yield. The use of spatial analysis and remote sensing tools are an alternative for indirect detection of injury and for understanding population dynamics of the pest in sugarcane, so they can become instrumental for decision-making on an integrated pest management program.

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“…The resulting reduction in insecticide usage can slow the development of resistance, in addition to preserving beneficial insects by maintaining unsprayed refuges in fields (Midgarden et al 1997, Karimzadeh et al 2010). The availability of geographic information system tools has facilitated the development of spatially variable methods of sampling of insects in cotton and the application of insecticides (McKinion et al 2009, Deleon et al 2017). However, such methods have not yet been developed for thrips.…”

mentioning

confidence: 99%

Spatial Distributions of Thrips (Thysanoptera: Thripidae) in Cotton

Reay‐Jones

Greene

Bauer³

2019

Journal of Insect Science

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A 4-yr study was conducted to determine the degree of aggregation of thrips and injury in cotton, Gossypium hirsutum L., and their spatial association with a multispectral vegetation index (normalized difference vegetation index [NDVI]) and soil apparent electrical conductivity (ECa). Using the Spatial Analysis by Distance IndicEs analyses (SADIE), adult thrips were significantly (P < 0.05) aggregated in 4 out of 24 analyses for adult thrips (17%), 4 out of 24 analyses for immature thrips (17%), and 2 out of 15 analyses for injury (13%). The SADIE association tool showed that NDVI values were associated with adult thrips in 2 out of 20 paired datasets (10%), with immature thrips in 3 out of 20 paired datasets (15%), and with thrips injury in 1 out of 14 paired datasets (7.1%). Soil ECa values were generally more associated with thrips variables than NDVI, with shallow ECa positively associated with adult thrips in 6 out of 21 paired datasets (28.6%), with immature thrips in 8 out of 21 paired datasets (40.0%), and with thrips injury in 8 out of 14 paired datasets (57.1%). The greater frequency of positive associations between thrips variables and soil ECa suggests a greater potential for site-specific management, particularly in the Coastal Plain of the southeastern United States, where soil types are highly variable.

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Use of a geographic information system to produce pest monitoring maps for south Texas cotton and sorghum land managers

Cited by 16 publications

References 15 publications

Kajian Algoritma Deteksi Hama Gudang Menggunakan Metode Pengukuran Intensitas Pantulan Cahaya Objek

Kajian Algoritma Deteksi Hama Gudang Menggunakan Metode Pengukuran Intensitas Pantulan Cahaya Objek

Population Dynamics and Estimation of Damage of the Spittlebug Aeneolamia varia on Sugarcane in Colombia by Using remote Sensing and Machine Learning Tools

Spatial Distributions of Thrips (Thysanoptera: Thripidae) in Cotton

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