Detection of Locusta migratoria and Nomadacris septemfasciata (Orthoptera: Acrididae) Using MobileNet V2 Quantized Convolution Neural Network, Kazungula, Zambia

Halubanza, Brian; Phiri, Jackson; Nyirenda, Mayumbo; Nkunika, Phillip O. Y.; Kunda, Douglas

doi:10.1007/978-3-031-09073-8_43

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…In ref. [27], a convolutional neural network has been applied for the detection of two species, African migratory locust (Locusta migratoria migratoriodes) and Red locust (Nomadacris septemfasciata), that are prevalent in the study area in Zambia. Images taken in situ are used to train and validate the results of locust detection in that work.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Classification–Regression Schemes for Desert Locust Presence Prediction in Western Africa

Cornejo-Bueno,

Pérez-Aracil,

Casanova-Mateo

et al. 2023

Applied Sciences

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For decades, humans have been confronted with numerous pest species, with the desert locust being one of the most damaging and having the greatest socio-economic impact. Trying to predict the occurrence of such pests is often complicated by the small number of records and observations in databases. This paper proposes a methodology based on a combination of classification and regression techniques to address not only the problem of locust sightings prediction, but also the number of locust individuals that may be expected. For this purpose, we apply different machine learning (ML) and related techniques, such as linear regression, Support Vector Machines, decision trees, random forests and neural networks. The considered ML algorithms are evaluated in three different scenarios in Western Africa, mainly Mauritania, and for the elaboration of the forecasting process, a number of meteorological variables obtained from the ERA5 reanalysis data are used as input variables for the classification–regression machines. The results obtained show good performance in terms of classification (appearance or not of desert locust), and acceptable regression results in terms of predicting the number of locusts, a harder problem due to the small number of samples available. We observed that the RF algorithm exhibited exceptional performance in the classification task (presence/absence) and achieved noteworthy results in regression (number of sightings), being the most effective machine learning algorithm among those used. It achieved classification results, in terms of F-score, around the value of 0.9 for the proposed Scenario 1.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Classification–Regression Schemes for Desert Locust Presence Prediction in Western Africa

Cornejo-Bueno,

Pérez-Aracil,

Casanova-Mateo

et al. 2023

Applied Sciences

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Low Cost IoT-Based Automated Locust Monitoring System, Kazungula, Zambia

Halubanza

Phiri

Nyirenda

et al. 2023

Lecture Notes in Networks and Systems

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LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction

Khan,

Akram,

Zafar

et al. 2024

PeerJ Computer Science

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The desert locust is one of the most destructive locusts recorded in human history, and it has caused significant food shortages, monetary losses, and environmental calamities. Prediction of locust attacks is complicated as it depends on various environmental and geographical factors. This research aims to develop a machine-learning model for predicting desert locust attacks in 42 countries that considers three predictors: soil moisture, maximum temperature, and precipitation. We developed the Global Locust Attack Database for 42 countries (GLAD42) by integrating TerraClimate’s environmental data with locust swarm attack data from the Food and Agriculture Organization (FAO). To improve the usability of spatial data, reverse geocoding which is the process of converting geographic coordinates (longitude and latitude) into human-readable location names (such as countries and regions) was employed. This step enhances the clarity and interpretability of the data by providing meaningful geographic context. This study’s initial dataset focused on instances where locust attacks were recorded (positive class). To ensure a comprehensive analysis, we also incorporated negative class instances, representing periods (specific years and months) in the same countries and regions where locust attacks did not occur. This research utilizes the benefits of lazy learners by employing the K-nearest neighbor algorithm (K-NN), which provides high accuracy and the benefit of no time-consuming retraining even if real-time updated data is periodically added to the system. This research also focuses on building an eco-friendly machine learning model by evaluating carbon emissions from ML models. The results obtained from LocustLens are compared with other machine learning models, including baseline–K-NN, decision trees (DT), Logistic regression (LR), AdaBoost Classifier, BaggingClassifier, and support vector classifier (SVC). LocustLens outperformed all competitors with an accuracy of 98%, while baseline-K-NN achieved 96%, SVC gave 91%, DT gave 97%, AdaBoost has accuracy of 91%, BaggingClassifier gave 94% and LR gave 83%, respectively. Carbon emissions from RAM and CPU electricity consumption are measured in kg gCO2. They are a minimum for AdaBoost Classifier equal to 0.02 and 0.07 for DT and a maximum of 9.03 for SVC. The carbon footprint of LocustLens is 4.87 kg gCO2.

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Detection of Locusta migratoria and Nomadacris septemfasciata (Orthoptera: Acrididae) Using MobileNet V2 Quantized Convolution Neural Network, Kazungula, Zambia

Cited by 3 publications

References 21 publications

Machine Learning Classification–Regression Schemes for Desert Locust Presence Prediction in Western Africa

Machine Learning Classification–Regression Schemes for Desert Locust Presence Prediction in Western Africa

Low Cost IoT-Based Automated Locust Monitoring System, Kazungula, Zambia

LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction

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