Multi-Scale Remote Sensing for Fall Armyworm Monitoring and Early Warning Systems

Buchaillot, Ma. Luisa; Cairns, Jill E.; Hamadziripi, Esnath; Wilson, Kenneth; Hughes, David; Chelal, John; McCloskey, Peter; Kehs, Annalyse; Clinton, Nicholas; Cressman, Keith; Araus, José Luís; Kefauver, Shawn C.

doi:10.1109/igarss39084.2020.9323181

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Tageldin et al [37] used machine learning to forecast caterpillar (Spodoptera littoralis) attacks on cotton plants with 84% accuracy. Another recent addition showed how remote sensing can quantify fall armyworm damage [38].…”

Section: Image Processingmentioning

confidence: 99%

Disease Identification using Deep Learning in Agriculture: A Case Study of Cotton Plant

Hassan,

Malik,

Irtaza

et al. 2022

VFAST trans. softw. eng.

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Among all the agrician products, cotton is known as “Ready Cash Crop” and it plays the significant role in the stability of the economy of a country. Therefore, it is extremely important to monitor the cotton crop from the numerous diseases. Unfortunately, sometimes human eyes not be able to analyze these diseases at earlier stage and that will affect not only the quality and also the quantity of the cotton crops. To address this early monitoring issue we proposed an interactive framework based on target feature extraction and deep learning model for cotton leaf screening to deal with these well-known dangerous diseases; Grey Mildew, Cercospora, Bacterial Blight and Alternaria. In this study we chosen our own collected dataset that contains 522 images of cotton leaves that were collected from the field (Cotton agricultural areas near the Multan city). The performance evaluation matric indicates the algorithm secure; 85.42% overall accuracy, 0.8542 precision, 0.8542 recall, 0.854 F1 score and 0.817 kappa coefficient indicates the generalization and acceptability of the model. The proposed framework not only assists the agronomist but also the farmer because of early identification of diseases from cotton crop and to avoid from the massive loss. It make better decisions for cotton crop management and contributes in the sustainability of the economy.

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Section: Image Processingmentioning

confidence: 99%

Disease Identification using Deep Learning in Agriculture: A Case Study of Cotton Plant

Hassan,

Malik,

Irtaza

et al. 2022

VFAST trans. softw. eng.

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“…Earlier studies have concentrated on the bio-ecological insights of the FAW i.e., the ecologically preferred conditions, migratory behavior, morphology, and biological development [ 12 , 26 , 27 ]. Again, more literature has focused on FAW surveillance and monitoring [ 28 ], its potential impacts on crop production [ 8 , 14 , 25 ], the potential management strategies including farming systems [ 15 ], modeling the potential pest population growth [ 23 ] and detecting crop damage caused by the pest using remotely sensed data [ 29 , 30 ]. Besides, most research that have predicted FAW habitat suitability have used the maximum entropy (MaxEnt) and CLIMEX ecological niche modeling (ENM) approaches at global or continental scales [ 22 , [31] , [32] , [33] ].…”

Section: Introductionmentioning

confidence: 99%

Computational biogeographic distribution of the fall armyworm (Spodoptera frugiperda J.E. Smith) moth in eastern Africa

Abdel‐Rahman

Kimathi²,

Mudereri³

et al. 2023

Heliyon

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“…There are two primary early warning systems for invasive flying pests in eastern Africa—the eLocust system [ 15 ] and the FAW Monitoring and Early Warning System (FAMEWS) [ 16 ]—both of which are funded by the United Nations Food and Agriculture Organization (FAO). These systems rely upon a mobile application or dedicated satellite-connected tablet that allows for the collection, processing, storing, and disseminating of observational data on pest presence and impact across wide areas.…”

Section: Introductionmentioning

confidence: 99%

Designing a Pest and Disease Outbreak Warning System for Farmers, Agronomists and Agricultural Input Distributors in East Africa

Brown

Mugo²,

Petersen

et al. 2022

Insects

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Early warnings of the risks of pest and disease outbreaks are becoming more urgent, with substantial increases in threats to agriculture from invasive pests. With geospatial data improvements in quality and timeliness, models and analytical systems can be used to estimate potential areas at high risk of yield impacts. The development of decision support systems requires an understanding of what information is needed, when it is needed, and at what resolution and accuracy. Here, we report on a professional review conducted with 53 professional agronomists, retailers, distributors, and growers in East Africa working with the Syngenta Foundation for Sustainable Agriculture. The results showed that respondents reported fall armyworm, stemborers and aphids as being among the most common pests, and that crop diversification was a key strategy to reduce their impact. Chemical and cultural controls were the most common strategies for fall armyworm (FAW) control, and biological control was the least known and least used method. Of the cultural control methods, monitoring and scouting, early planting, and crop rotation with non-host crops were most used. Although pests reduced production, only 55% of respondents were familiar with early warning tools, showing the need for predictive systems that can improve farmer response.

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Multi-Scale Remote Sensing for Fall Armyworm Monitoring and Early Warning Systems

Cited by 5 publications

References 7 publications

Disease Identification using Deep Learning in Agriculture: A Case Study of Cotton Plant

Disease Identification using Deep Learning in Agriculture: A Case Study of Cotton Plant

Computational biogeographic distribution of the fall armyworm (Spodoptera frugiperda J.E. Smith) moth in eastern Africa

Designing a Pest and Disease Outbreak Warning System for Farmers, Agronomists and Agricultural Input Distributors in East Africa

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