Prediction of desert locust breeding areas using machine learning methods and SMOS (MIR_SMNRT2) Near Real Time product

Gómez, Diego; Salvador, Pablo; Sanz, Julia; Rodrigo, Juan Fernando; Gil, Jorge; Casanova, José

doi:10.1016/j.jaridenv.2021.104599

Cited by 16 publications

(8 citation statements)

References 24 publications

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“…Desert locust behaviour and dispersal patterns are complex processes that depend on environmental factors such as temperature [36], precipitation [22,37], the condition of vegetation [38][39][40], soil moisture [41,42], soil composition [41] and the wind [36]. These factors can create either stimulatory or inhibitory conditions for gregarisation, desert locust migration and breeding.…”

Section: Discussionmentioning

confidence: 99%

Regional Differences in Control Operations during the 2019–2021 Desert Locust Upsurge

et al. 2021

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The desert locust remains a major threat to global food security. Control operations are a crucial tool to manage crisis; this research investigated the nature of control operations conducted between 2019–2021. Historical data on desert locust and control operations were obtained from the survey reports at the FAO Locust Hub and analysed with respect to survey reports, land cover types, cropland/rangeland extent and crop productivity data. We found that 16.1% of the grid cells with locust presence and 14.9% of the grid cells with control operations had a proportion of rangeland higher than 0.75; while 13.3% of the grid cells with locust presence and 13.2% of the grid cells with control operations had a proportion of croplands higher than 0.75, highlighting that locust presence and control operations were reported in both rangeland and cropland. Control operations continue to be used both to reduce overall locust numbers and to protect crops. Furthermore, through identifying which crops were most at risk, our analyses indicate that wheat production was under the highest strain during periods of increased locust infestations.

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Section: Discussionmentioning

confidence: 99%

Regional Differences in Control Operations during the 2019–2021 Desert Locust Upsurge

et al. 2021

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“…These authors trained a CNN on spectrographic images of long‐lasting outdoor sound recordings to automatically detect the sounds of flying insects' buzzing and woodpeckers' drumming as they forage and call. Another study used a combination of AI‐enabled sensors and satellite imagery to monitor the population dynamics of desert locusts in West Africa (Gómez et al., 2021). Their results suggest that soil moisture data retrieved between 95 and 12 days (before the sighting) provided sufficient information to achieve acceptable predictive performances about possible outbreaks.…”

Section: Fields That Benefit From Ai Methodsmentioning

confidence: 99%

Artificial neuronal networks are revolutionizing entomological research

Hartbauer

2024

J Applied Entomology

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The application of artificial intelligence (AI) in entomological research has gained significant attention in recent years. This review summarizes the current state of research on the potential of AI methods in various subfields of entomology, such as behavioural biology, biodiversity research, climate change research, pest management, and disease vector control. In some cases, AI‐based species identification methods based on deep learning neuronal network models have been shown to outperform traditional morphological identification methods in terms of accuracy and speed. Behavioural biology research has been enhanced through the use of AI‐based tracking systems that can classify insect behaviour and movement patterns. Habitat modelling has also been improved with the use of AI, allowing for the creation of more accurate models that can predict insect distribution and abundance. Climate change and biodiversity research have benefited from AI‐driven tools that can analyse large datasets and predict the impact of environmental changes on insect populations. In agriculture, pest management has been revolutionized by AI methods, with the development of smart traps and monitoring systems that can detect and identify pest species in real‐time, enabling targeted control measures. Disease vector control has also been improved through the use of AI‐based predictive models, which can identify areas at risk of disease transmission and aid in the development of effective control strategies. In conclusion, AI methods have the potential to revolutionize many aspects of entomological research. Future research should focus on developing more sophisticated AI tools and integrating them into entomological research to address even more complex ecological questions.

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“…Machine learning-based pest prediction is often implemented using the k nearest neighbors algorithm, which searches for the k closest samples in the feature space. Gómez et al [47] build and compare the performance of six machine learning algorithms to predict desert locusts based on soil moisture and demonstrate that the model prediction performance is limited by the space and time of the data.…”

Section: Time-series Data-based Approachesmentioning

confidence: 99%

Using Time-Series Generative Adversarial Networks to Synthesize Sensing Data for Pest Incidence Forecasting on Sustainable Agriculture

2023

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A sufficient amount of data is crucial for high-performance and accurate trend prediction. However, it is difficult and time-consuming to collect agricultural data over long periods of time; the consequence of such difficulty is datasets that are characterized by missing data. In this study we use a time-series generative adversarial network (TimeGAN) to synthesize multivariate agricultural sensing data and train RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit) neural network prediction models on the original and generated data to predict future pest populations. After our experiment, the data generated using TimeGAN and the original data have the smallest EC value in the GRU model, which is 9.86. The results show that the generative model effectively synthesizes multivariate agricultural sensing data and can be used to make up for the lack of actual data. The pest prediction model trained on synthetic data using time-series data generation yields results that are similar to that of the model trained on actual data. Accurate prediction of pest populations would represent a breakthrough in allowing for accurate and timely pest control.

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Prediction of desert locust breeding areas using machine learning methods and SMOS (MIR_SMNRT2) Near Real Time product

Cited by 16 publications

References 24 publications

Regional Differences in Control Operations during the 2019–2021 Desert Locust Upsurge

Regional Differences in Control Operations during the 2019–2021 Desert Locust Upsurge

Artificial neuronal networks are revolutionizing entomological research

Using Time-Series Generative Adversarial Networks to Synthesize Sensing Data for Pest Incidence Forecasting on Sustainable Agriculture

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