# A Geospatial Approach to Predicting
Desert Locust Breeding Grounds in Africa

Ibrahim Salihu Yusuf  Mukhtar Opeyemi Yusuf  Kobby Panford-Quainoo  Arnu
Pretorius

###### Abstract

Desert locust swarms present a major threat to agriculture and food security.
Addressing this challenge, our study develops an operationally-ready model for
predicting locust breeding grounds, which has the potential to enhance early
warning systems and targeted control measures. We curated a dataset from the
United Nations Food and Agriculture Organization’s (UN-FAO) locust observation
records and analyzed it using two types of spatio-temporal input features:
remotely-sensed environmental and climate data as well as multi-spectral earth
observation images. Our approach employed custom deep learning models (three-
dimensional and LSTM-based recurrent convolutional networks), along with the
geospatial foundational model Prithvi recently released by (Jakubik et al.,
2023). These models notably outperformed existing baselines, with the Prithvi-
based model, fine-tuned on multi-spectral images from NASA’s Harmonized
Landsat and Sentinel-2 (HLS) dataset, achieving the highest accuracy, F1 and
ROC-AUC scores (83.03%, 81.53% and 87.69%, respectively). A significant
finding from our research is that multi-spectral earth observation images
alone are sufficient for effective locust breeding ground prediction without
the need to explicitly incorporate climatic or environmental features.

Machine Learning, Geospatial ML, Remote Sensing

##  1 Introduction

Desert Locusts (DL) are voracious migratory pests that can form large swarms,
causing significant damage to crops and leading to food crises that affect
humans and livestock (Peng et al., 2020). Over the years, DL have posed a
significant threat to food security in Africa. Their ability to swiftly
migrate over long distances and across geographical boundaries, makes it
extremely difficult to coordinate preventative measures between control teams.
Although various attempts have been made to mitigate the DL threat (Enns et
al., 2022), much remains to be done.

DL exhibit phase polymorphism (also called polyphenism) which causes them to
change from a solitarious to a gregarious phase and vice versa. Solitarious DL
are shy, sedentary and do not move much, and with their green or brownish
cryptic colour hide during the day (Pflüger & Bräunig, 2021). They also avoid
other locusts, except for mating, and are reported to migrate at night
(Uvarov, 1977; Pflüger & Bräunig, 2021). In contrast, gregarious DL are
conspicuous and reveal anti-predator warning colours in bright yellow and
black (Pflüger & Bräunig, 2021). Gregarious locusts are very active, and they
aggregate both as nymphs (marching hopper bands) and adults (swarms), and they
fly by day and roost overnight in trees (Pflüger & Bräunig, 2021). It has been
observed that a major stimulus for polyphenism in solitarious DL is crowding.
When solitarious DL are crowded, regularly touching the hind femur of one
another, or perceiving the smell and sight of others, this causes a
solitarious individual to become gregarious (Rogers et al., 2003; Anstey et
al., 2009). When adult locusts become gregarious they form large swarms; a
swarm measuring a single square kilometre can contain up to 80 million
individuals (or more) that can fly up to 90 miles a day and consume an
equivalent of their body mass in green vegetation every day (Uvarov, 1957).
This is equivalent to the food that would be consumed by 35,000 people. As
they migrate in search of food, each female lays up to 80 eggs in a pod. If
the environmental conditions are suitable for breeding, the population of DL
can grow exponentially within a short period of time, thereby resulting in a
plague.

Historically, DL plagues have caused significant harm to both human and animal
lives (Gross, 2021; Mullié et al., 2023). Various initiatives led by the UN-
FAO and other stakeholders have aimed to mitigate this threat. Despite these
efforts, there remain opportunities to enhance the effectiveness of measures
against the adverse impact of DL on both animal and crop production. Given the
nature of their lifecycle, whereby adults lay eggs that hatch in about 2 weeks
and grow to become adults that form swarms in about 4 months, there is a need
to develop a comprehensive solution to mitigating locust plagues. The first
stage in locusts’ lifecycle is oviposition by adult females which later
results in hoppers that have limited locomotion abilities (Symmons & Cressman,
2001).

During this phase, known as the breeding stage, directing control efforts
towards eradicating locust breeding appears to be a more effective approach.
If successful at this stage, it means that some future generations of locusts
have been eliminated and, if consistently carried out, after some years it is
possible to reduce the negative impact of locust plagues to a bare minimum.
This approach would first involve the identification of actual locust breeding
grounds and subsequently the application of an effective control operation
such as pesticide spraying.

In this paper, we present a set of new custom deep learning architectures as
well as a fine-tuned foundational model specifically for DL breeding ground
prediction. These models were created by first curating a breeding dataset
from locust observation data collected by the UN-FAO and then exploring new
modelling strategies using various remotely-sensed features and multi-spectral
earth observation images. Whilst past research aimed at identifying regions
_favourable_ to breeding, our focus is more specific. We care about
identifying regions where locusts would be found _present_ , copulating and/or
laying eggs, i.e. actual breeding grounds across Africa. However, even with
this more specific task, we show that our models significantly outperform
existing baselines, achieving accuracy, F1 and ROC-AUC scores of 83.03%,
81.53% and 87.69%, respectively. By openly releasing our models, we hope this
work might assist locust control agencies and potentially improve early
warning systems and the effectiveness of targeted control measures.

###  1.1 Related Work

Several researchers have approached locust breeding ground prediction using
machine learning (Gómez et al., 2018; Kimathi et al., 2020; Gómez et al.,
2021; Klein et al., 2022). Here we give a brief overview of this work.

Machine learning has been instrumental in assessing the role of soil moisture
(SM) in predicting DL breeding grounds. (Gómez et al., 2018) used a machine
learning approach to predict DL breeding grounds based on SM data from
European Space Agency Climate Change Initiative (ESA-CCI) 111https://esa-
soilmoisture-cci.org/. In their study area of Mauritania, and period of
1985-2015, random forest achieves the best performance in evaluating the link
between SM and DL hoppers’ presence with Kappa and ROC-AUC scores of 0.95 and
0.74 respectively. Their study shows that ESA-CCI SM was a significant
predictor of DL breeding grounds in areas around Mauritania. They also
demonstrate that a location is deemed favourable for breeding when the SM
minimum value is over 0.07 $m^{3}/m^{3}$ within 6 days or more. To improve
their work, (Gómez et al., 2021) addressed the lack of automated and
operational procedures for predicting DL breeding grounds in near real-time
(NRT). They indicated that the ESA-CCI SM data is released with several months
of lag. Therefore, they used SM data from the Soil Moisture and Ocean Salinity
(SMOS) satellite data product222https://earth.esa.int/eogateway/catalog/smos-
nrt-data-products, which updates three times every month, to predict DL
breeding grounds. Their study area covers the entire DL recession area between
2016–2018, which includes more than 30 countries from West Africa to West
India, spanning about 16 million square kilometers. They evaluated six machine
learning models and found a Weighted k-Nearest Neighbors approach to have the
best performance with a Kappa and ROC-AUC score of 0.50, and 0.80
respectively.

Some studies have combined SM data along with other bio-climatic data to
improve model generalization. (Kimathi et al., 2020) is one of these studies
that utilizes machine learning algorithms to predict potential DL breeding
grounds in East Africa. They considered key bio-climatic factors such as
temperature and rainfall, as well as edaphic factors such as sand and moisture
contents. Using the MaxEnt algorithm, they trained models on Morocco,
Mauritania and Saudi Arabia and found Morocco model parameters to have the
best generalization with an AUC score of 0.82. The findings of this study
revealed that vast areas of Kenya and Sudan, along with the northeastern
regions of Uganda, and the southeastern and northern regions of South Sudan,
were at a high risk of providing a suitable breeding environment for DL in the
period between February and April 2020.

So far, the data considered in all of these studies are temporal and vary
rapidly over time. Some studies have incorporated certain static ecological
data that can be useful for DL breeding survival. (Klein et al., 2022)
introduce a fused multi-scale approach for predicting suitable breeding
habitat for different locust species in different areas, including Calliptamus
italicus, CIT (Italian locust) in Pavlodar oblast (Kazakhstan), Dociostaurus
maroccanus, DMA (Moroccan locust) in Turkistan oblast (Kazakhstan), and
Schistocerca gregaria (Desert Locust) in the Awash river basin (Ethiopia,
Djibouti, Somalia). They incorporated up-to-date land surface parameters,
vegetation development, and other relevant environmental factors into their
model, along with climatic and soil preferences derived from ecological niche
modelling (ENM). They emphasize the importance of considering actual changes
in the landscape and human interactions for understanding locust outbreaks and
defining suitable breeding grounds. To address this, the authors propose
incorporating variables obtained from
Sentinel-2333https://sentinel.esa.int/web/sentinel/missions/sentinel-2 (high-
resolution remote sensing data) time-series analysis to describe the current
state of the land, thereby refining the suitable breeding grounds within the
model. For the year 2019, the model was validated using field observations and
achieved an AUC performance of 0.747 for CIT, 0.850 for DMA and 0.801 for DL.

Although classical machine learning models have shown promise for locust
control, their ability to handle complex temporal data is limited. In
contrast, certain deep learning models are well suited for such data. As an
example, (Tabar et al., 2021) introduced the Predictor of Locust Activity and
movemeNt (PLAN) model, a deep learning approach to forecasting DL migration
patterns by integrating crowdsourced observations (data from
PlantVillage444https://plantvillage.psu.edu/) and remote-sensed data. Inputs
to their model are categorized into three categories: (1) static variables
(actual evapotranspiration, sand content, total biomass productivity and
elevation), (2) temporal variables (soil moisture, precipitation and wind
speed) and (3) historical locust observations. Each category is processed by a
different sub-component of the model and later fused together for the final
prediction. Their model achieved an AUC score of 0.89 in forecasting the
presence of DL.

Previous studies on desert locust breeding ground prediction have considered
different datasets, input variables and study areas, making it difficult to
benchmark the performance of the model from each study. The remotely-sensed
variables used are also from different satellite data products with varying
spatial and temporal resolutions as well as update frequency. These
compounding issues make it difficult to build a robust and operationally-ready
model that can be used by regional governments and control agencies such as
the UN-FAO for administering control activities towards eliminating the threat
posed by DL. In this study, our goal is to build such a robust and
operationally-ready model for predicting DL breeding grounds.

##  2 Methods

Locust breeding ground prediction is framed as a binary classification task.
We define a set of geographical locations as $\mathcal{L}$, where each
location $l$ has a specific coordinate. Each location $l$ has an associated
binary label $y_{l}$, indicating if it’s an actual breeding ground ($y_{l}=1$)
or not ($y_{l}=0$). For every location, a feature vector $x_{l}$ is introduced
to represent spatio-temporal data. This vector incorporates components
including temporal variables and non-temporal variables.

In essence, the goal is to learn a function
$f:\mathcal{X}\rightarrow\\{0,1\\}$ based on labeled data
$\\{(x_{l},y_{l})\\}$, predicting the breeding ground label for any feature
vector $x_{l}$. The subsequent sections detail the data preparation for this
problem.

###  2.1 Data

We present our data sources, collection methods, and the final data processing
used in our models.

####  2.1.1 Locust Observation Records.

To learn the behaviour of locusts that would help in identifying breeding
grounds, there is a need to have enough locust observation data. While there
are locust observation data collected by regional authorities in some
countries, they are limited in their spatial and temporal coverage and
accessibility. The primary source of locust observation data that overcomes
the aforementioned limitations is the UN-FAO Locust Hub555https://locust-hub-
hqfao.hub.arcgis.com. The UN-FAO has been collecting locust observation data
over the past 48 years (1975-Present) spanning different locust stages
(Hoppers, Bands, Adults, and Swarms) across Africa and Asia. The data contains
geolocation records and other environmental conditions of the observed site
and is made publicly available via the Locust Hub. The data is collected based
on guidelines recommended by the UN-FAO (Keith, 2001) to be used during field
surveys.

####  2.1.2 Breeding Data.

The locust observation data from the FAO describes DL observed at different
stages (Hoppers, Bands, Adults, and Swarms). For our use case, we are
interested in the records that describe the breeding stage. We curate the
appropriate data for this stage in a manner similar to (Klein et al., 2022) by
including records of adults found laying eggs and those of early-stage instars
(insect development stage), which also depict successful incubation and nymph
hatching (Klein et al., 2022).

In the domain of species distribution modeling, data collected from the field
are mostly presence records. However, the UN-FAO data includes observations of
sites where locusts were expected to be found (favorable locations) but were
missing. We wanted to consider these records as a subset of the absence
records, but from our preliminary experiments, we found them to be biased. As
a result, similar to (Gómez et al., 2021), we discarded them and opted to
generate pseudo-absence records as prevalent in previous studies (Gómez et
al., 2018, 2021; Klein et al., 2022). We perform pseudo-absence generation
using a random sampling technique (Iturbide et al., 2015; Yusuf et al., 2022)
while maintaining a buffer zone around each presence observation. A
combination of presence and pseudo-absence records, described in table 1,
makes up a robust dataset for learning to classify actual locust breeding
grounds. Figure 1 also shows a visual representation of our curated breeding
data.

Table 1: Description of Curated Breeding Records

Split | Non-Breeding (0) | Breeding (1) | Date Range
---|---|---|---
Train | 2238 | 2238 | 2020-01-01 to 2021-04-21
Validation | 154 | 154 | 2021-04-22 to 2021-07-09
Test | 820 | 820 | 2021-07-10 to 2023-07-30

Figure 1:  Visualization of Breeding Data. Green and Red points represent non-
breeding and breeding locations respectively. There is an equal number of
green and red points. While breeding locations are concentrated, non-breeding
locations are spread out due to random sampling during pseudo-absence
generation.

###  2.2 Input Features.

Using the breeding data created in 2.1.2, which contains only geolocation and
time information, we derive input features from remotely-sensed environmental
and climatic variables as well as earth observation data. In each case, we
prioritize satellite data products with higher spatial-temporal resolution and
update frequency.

####  2.2.1 Remotely-Sensed Variables

Similar to previous studies, we sourced environmental and climatic features
that are suitable for locust breeding. These features are either static or
temporal. We drew inspiration from (Tabar et al., 2021) and crafted them into
a spatio-temporal data representation. By dividing the earth’s surface into a
grid of 0.1-degree resolution, we extracted an $n\times n$ grid centered on
the observed location, where $n$ is odd with a default value of 7. Two data
feature groups were prepared:

  * 1

Temporal: Incorporating soil moisture, precipitation, and fraction of
vegetation cover, we constructed a spatio-temporal representation. For each
record in our data, we retrieved a 96-day historical record of each temporal
variable and resampled the data by computing a mean for each 3-day period.
Following the resampling process, we obtained 30 time-steps for each variable,
resulting in an input of shape $30\times 7\times 7\times 3$.

  * 2

Non-temporal: Combining all 14 variables from the TerraClimate
666https://www.climatologylab.org/terraclimate.html with SoilGrid’s
sand_5-15cm_mean 777https://www.isric.org/explore/soilgrids, Copernicus
land_cover_2019 888https://land.copernicus.eu/global/products/lc and ALOS
WORLD 3D - 30m wadis
999https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm data products,
we constructed a matrix of $7\times 7\times 17$.

Figure 2: Visual Representation of Spatiotemporal Input Features. The spatial
dimensions are denoted by $x$ and $y$, while $T$ represents the temporal
dimension. $v$ and $s$ indicate the number of temporal and non-temporal
variables, respectively. (A) corresponds to the input fed into our PLAN-LB
model. (B) shows the spatiotemporal representation utilized by our Conv3D and
ConvLSTM models. Lastly, (C) presents the input employed for SVM and logistic
regression models.

Figure 2 visually outlines these feature groups. Here $x$, and $y$ denote
spatial dimensions, and $T$ is the temporal dimension (the number of time-
steps after resampling). $v$ and $s$ are the number of temporal, and non-
temporal features, respectively. The individual representation in (A) is fed
to our PLAN-based model while the spatio-temporal representation in (B) is fed
to the other deep learning models that utilize remotely-sensed input features.
For our classical models, we flatten each data group and concatenate them
before feeding the input to the model, as illustrated in (C).

Figure 3: PLAN-LB Model Architecture.  This model was derived from PLAN’s
model and it independently processes the temporal and non-temporal inputs. The
temporal module encodes each entry in the temporal series into a feature
vector. Subsequently, the series of resulting feature vectors undergo
processing via an LSTM block in a many-to-one configuration. The non-temporal
module similarly encodes the non-temporal input into a feature vector. The
outputs from both modules are then concatenated and forwarded to a final
linear layer for classification

####  2.2.2 Earth Observation Data

Unlike remotely sensed features such as soil moisture, elevation etc. earth
observation data are multi-spectral images of the earth’s surface. These
images have spectral bands ranging from visible light to the near-infrared
(NIR) and shortwave infrared (SWIR) part of the electromagnetic spectrum. This
enables detailed observations of vegetation, soil and water cover, inland
waterways, and coastal areas. We hypothesize that the information contained in
these spectral images can serve the same purpose as remotely-sensed features
in detecting desert locust breeding grounds.

NASA’s Harmonized Landsat and Sentinel-2 (HLS) (Claverie et al., 2018)
101010https://hls.gsfc.nasa.gov/ dataset, which provides a high temporal (2-3
days) and spatial (30m) multi-spectral image of the earth’s surface makes a
suitable source of input features for an operational locust breeding ground
prediction model. Using the HLS satellite data and our curated breeding
records, we create spatio-temporal “chips” (subsection of a larger geospatial
image) of size $224\times 224$ for locust breeding ground prediction which
comprise 3 temporal steps with a size of 30 days. Each step also includes 6
multi-spectral bands (Blue, Green, Red, Narrow NIR, SWIR 1, SWIR 2). This
implies that the resulting input features of shape $3\times 6\times 224\times
224$ represent an observation of a specific area over the past 90 days.

Figure 4: Conv3D Model Architecture.  Our Conv3D model features two residual
layers with Conv3D blocks, layer normalization, and ReLU activation. It
employs a kernel size of $(3,7,7)$ and retains spatial dimensions using a
“same” padding approach. The model concludes with average pooling and a
softmax activation output layer, producing a probability score for locust
breeding likelihood. Given that the input features pertain to a specific point
location, the output provides a classification indicating whether the point is
a breeding or non-breeding ground.

###  2.3 Models

In this section, we present deep learning models designed to model the complex
relationships between the various spatio-temporal features from the two
categories of data discussed in the previous section. By leveraging deep
learning, our models aim to uncover intricate patterns and dependencies within
the input features that will aid in locust breeding ground prediction. In the
following subsections, we describe the architecture and functioning of these
models.

####  2.3.1 PLAN for Locust Breeding (PLAN-LB)

This model is a modified version of the PLAN model (Tabar et al., 2021). Since
our input features do not include historical locust observations, as seen in
PLAN, we employ only two modules for processing the temporal and non-temporal
input. As explained in Section 2.2.1, our inputs have spatial dimensions of
$7\times 7$ to incorporate neighboring information, in contrast to the point
values utilized in PLAN. Consequently, the two modules we utilized are
convolutional modules, as depicted in Figure 3. The temporal module
independently encodes each entry in the temporal series into a feature vector.
Subsequently, the series of resulting feature vectors undergo processing via
an LSTM block in a many-to-one configuration. The non-temporal module
similarly encodes the non-temporal input into a feature vector. The outputs
from both modules are then concatenated and forwarded to a final linear layer
for classification.

####  2.3.2 3D convolutional network (Conv3D)

This model learns from the spatio-temporal input features using a three-
dimensional convolutional network architecture. Conv3D has been effective in
domains like action recognition, medical analysis, and geospatial analysis
(Wang et al., 2018; Wu et al., 2018; Lee et al., 2018; Liu & Hu, 2019; Lee et
al., 2021; Duan et al., 2022). Our Conv3D model features two residual layers
with Conv3D blocks, layer normalization, and ReLU activation. It employs a
kernel size of $(3,7,7)$ and retains spatial dimensions using a “same” padding
approach. The model concludes with average pooling and a softmax activation
output layer, producing a probability score for locust breeding likelihood.
The full architecture is displayed in Figure 4.

####  2.3.3 Convolutional LSTM (ConvLSTM)

A convolutional variant of the standard LSTM (Long Short-Term Memory)
(Hochreiter & Schmidhuber, 1997) recurrent network, ConvLSTM incorporates
convolutional operations within the recurrent structure, both in the input-to-
state and state-to-state transitions (Shi et al., 2015). It is specifically
designed to capture spatio-temporal dependencies in data and has been
effective in applications such as video analysis and weather forecasting (Shi
et al., 2015; Sanchez-Caballero et al., 2020; Moishin et al., 2021). Our
ConvLSTM uses a kernel size of $(3,3)$ for convolutional operations, followed
by a ReLU activation. A linear layer then transforms its outputs, and a
softmax activation provides a probability score for locust breeding
likelihood. The architecture is visualized in Figure 5.

Figure 5: ConvLSTM Model Architecture.  Our ConvLSTM uses a kernel size of
$(3,3)$ for convolutional operations, followed by a ReLU activation. A linear
layer then transforms its outputs, and a softmax activation provides a
probability score for locust breeding likelihood. Given that the input
features pertain to a specific point location, the output provides a
classification indicating whether the point is a breeding or non-breeding
ground.

####  2.3.4 Prithvi for Locust Breeding (Prithvi-LB)

Prithvi (Jakubik et al., 2023) is a ViT-based geospatial foundational model
pre-trained on HLS data. It features a self-supervised encoder with a ViT
architecture (Dosovitskiy et al., 2021), incorporating a Masked AutoEncoder
(MAE) learning strategy and an MSE loss function. The model exhibits spatial
attention across patches and introduces temporal attention. Prithvi
demonstrates superior performance in diverse remote sensing temporal tasks,
such as multi-temporal cloud gap imputation, floods and wildfire scars
segmentation, and multi-temporal crop segmentation (Jakubik et al., 2023).
Leveraging Prithvi’s capabilities, we derive a model that incorporates
Prithvi’s pre-trained ViT encoder and transpose convolution decoder blocks as
illustrated in Figure 6. Our Prithvi-LB model was trained to learn the
temporal and spatial intricacies of predicting locust breeding grounds using
HLS data.

Figure 6: Prithvi-LB Model Architecture.  This custom model was derived by
adding a custom decoder layer atop the pre-trained Prithvi vision transformer
encoder. The custom decoder consist of a stack of upsampling blocks followed
by a final two-dimensional convolutional layer that produces the output
segmentation map. The segmentation map classifies each 30-square-meter patch
of land as either a breeding or non-breeding ground.

##  3 Experiments

This section details the training of our models on both categories of input
features described in 2.2. We first outline our experimental framework and
then discuss our experiments.

###  3.1 Experimental setup

We utilized TPU_V3’s for training our ConvLSTM from the Google Cloud platform.
Complementing this, our computational setup included 4 vCPUs and 32GB of RAM.
For hyperparameter selection, we used a batch size of 32 and trained the
models for 200 epochs. Early stopping with a patience of 10 epochs was
employed to prevent overfitting. The learning rate was set to $1e-4$, and we
utilized the Adam optimizer with $\beta_{1}$ and $\beta_{2}$ values of $0.9$
and $0.999$, respectively.

For the Prithvi-LB model, we used an Nvidia V100 GPU with 8 vCPUs and 30GB of
RAM. We fine-tuned the model for 10 epochs with early stopping. The learning
rate was set to $1e-4$, and we utilized the AdamW optimizer with $\beta_{1}$,
$\beta_{2}$ and weight_decay values of $0.9$, $0.999$ and 0.1 respectively.

###  3.2 Using Remotely-Sensed Input Features

In this experiment, we trained three of our proposed models (PLAN-LB, Conv3D
andConvLSTM) and two classical machine learning models—Logistic Regression and
Support Vector Machine (SVM) on remotely-sensed input features described in
Section 2.2.1. Given the spatio-temporal nature of our data, preprocessing for
the classical models involved flattening and concatenating the input features.
Each of the models was trained to optimize the objective described in 2 and
the best model was selected using the validation split. The performance of the
selected model on the test set was evaluated using accuracy, precision,
recall, F1 and ROC-AUC scores. The results obtained from each model are
presented in Table 2.

Table 2: Experiment Results. RS and MS refer to remotely-sensed and multi-
spectral inputs, respectively. These results were obtained from training
different models described in Section 2.3 using various input types as
discussed in Section 2.2. The table presents performance metrics, including
accuracy, F1-score, precision, recall, and ROC-AUC score, for different models
in predicting DL breeding grounds. Notably, Prithvi-LB trained using multi-
spectral earth observation images yields the highest predictive performance.

Methods | Accuracy | F1-score | Precision | Recall | ROC_AUC | Input
---|---|---|---|---|---|---
SVM | 62.36 | 63.19 | 72.47 | 71.04 | 71.04 | RS
Logistic Regression | 60.23 | 60.13 | 69.45 | 67.96 | 67.96 | RS
PLAN-LB | 71.21 | 56.94 | 79.84 | 59.20 | 75.09 | RS
Conv3D | 75.38 | 64.74 | 86.29 | 64.67 | 69.91 | RS
ConvLSTM | 75.76 | 67.34 | 80.37 | 66.48 | 63.61 | RS
Prithvi-LB | 83.03 | 81.53 | 82.12 | 82.90 | 87.69 | MS

###  3.3 Using Multi-Spectral Earth Observation Images

We trained Prithvi-LB on spatio-temporal chips ($3\times 6\times 224\times
224$) derived from HLS data as described in 2.2.2. In this experiment, the
model was trained using a segmentation objective, where each pixel has a
probability score for the breeding and non-breeding class. The model was
trained for 10 epochs and the best checkpoint was selected using the
validation split. The results obtained after evaluating the selected
checkpoint on the test split are shown in Table 2.

##  4 Results and Discussion

The results from the experiments conducted in Sections 3.2 and 3.3 are
summarized in Table 2. In the initial set of experiments utilizing remotely-
sensed input features, the deep learning models outperformed the classical
models, with the ConvLSTM model emerging as the top performer. It achieved an
accuracy, F1-score, and ROC-AUC score of 75.76%, 67.34%, and 63.61%,
respectively.

On the other hand, the experiments involving HLS multi-spectral earth
observation data exhibited the best overall performance, surpassing ConvLSTM
with improvements of +5.11, +13.26, and +25.28 in accuracy, F1-score, and ROC-
AUC score, respectively. This outstanding performance can be attributed to
various factors, including the utilization of a pre-trained Prithvi ViT
encoder and the high spatial resolution (30m) of the data. Furthermore, HLS
boasts a high update frequency of 2-3 days, enhancing the model’s suitability
for operational deployment.

It is however noteworthy that a significant number of samples in the breeding
records dataset were lost due to missing values on one or more variables
during the preprocessing phase for remotely-sensed input features. This issue
not only affects the performance and reliability of remotely-sensed input
features but is also exacerbated by the low update frequency of the various
variable sources, rendering them less suitable for models intended for
operational deployment.

A visual analysis of the predictions made by Prithvi-LB is depicted in Figure
7. It predicts potential DL breeding sites across every $30m\times 30m$
section of our study area, as illustrated in Figure 1. The results demonstrate
the model’s capability to identify the sparse nature of DL breeding grounds
effectively. By overlaying these predictions on high-resolution satellite
imagery, it was observed that areas identified as probable DL breeding sites
predominantly consist of desert terrains with sparse tree cover. These areas
are presumed to be where DL edible vegetation might emerge following periods
of rainfall. To further substantiate the accuracy of our model’s predictions,
we plan to conduct ground-based verification in collaboration with the UN-FAO
and other partner organizations.

Figure 7: Visualization of Prithvi-LB Predictions for January 2023. This
figure presents the spatial predictions generated by the Prithvi-LB model for
January 2023, encompassing every $30m\times 30m$ parcel within the specified
study area, as depicted in Figure 1. Areas covered in red are predicted as
breeding sites. The model adeptly identifies the sparse nature of DL breeding
areas. Overlaying these predictions onto satellite imagery reveals that the
majority of areas predicted as potential breeding sites are characterized by
desert landscapes with sparse tree presence. This pattern suggests these
regions might witness vegetation growth following rainfall, indicating
possible DL breeding grounds.

##  5 Conclusion

In this research, we aimed to develop a robust and operationally-ready model
for predicting locust breeding grounds, addressing a critical need in managing
the threat posed by DL to animal and food security. Utilizing locust
observation records from the UN-FAO, along with two categories of input
features – remotely-sensed data and multi-spectral earth observation images –
we trained and evaluated various models. Our findings indicate that our
Prithvi-based model, which utilizes multi-spectral earth observation images,
demonstrates superior performance, attaining accuracy, F1 and ROC-AUC scores
of 83.03%, 81.53% and 87.69% respectively. This model’s effectiveness is
largely attributed to leveraging the high temporal (2-3 days) and spatial
(30m) resolution Harmonized Landsat and Sentinel-2 (HLS) satellite product.
Consequently, our research offers significant advancements in predicting
desert locust breeding grounds, with potential for enhancing the
administration and effectiveness of control activities undertaken by regional
governments and relevant agencies.

## Impact Statements

Our proposed methodology for detecting DL breeding grounds solely utilizing
multi-spectral earth observation images has not only surpassed existing
approaches but has also demonstrated the potential for immediate operational
deployment. This advancement addresses a critical need for organizations
involved in locust control operations, potentially enhancing their ability to
effectively mitigate the threat posed by DL. However, the DL threat is a
multi-stakeholder problem that needs complex coordination between many
partners and will not be solved by modeling alone. There are also implications
of relying solely on model predictions for administering locust control
activities, and such a strategy might fail to provide enough information to
effectively dispatch (often very expense) control measures.

## References

  * Anstey et al. (2009) Anstey, M. L., Rogers, S. M., Ott, S. R., Burrows, M., and Simpson, S. J.  Serotonin mediates behavioral gregarization underlying swarm formation in desert locusts.  _science_ , 323(5914):627–630, 2009.
  * Claverie et al. (2018) Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., and Justice, C.  The harmonized landsat and sentinel-2 surface reflectance data set.  _Remote Sensing of Environment_ , 219:145–161, 2018.  ISSN 0034-4257.  doi: https://doi.org/10.1016/j.rse.2018.09.002.  URL https://www.sciencedirect.com/science/article/pii/S0034425718304139.
  * Dosovitskiy et al. (2021) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N.  An image is worth 16x16 words: Transformers for image recognition at scale, 2021.
  * Duan et al. (2022) Duan, H., Zhao, Y., Chen, K., Lin, D., and Dai, B.  Revisiting skeleton-based action recognition.  In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , pp. 2969–2978, 2022.
  * Enns et al. (2022) Enns, C., Bersaglio, B., and Karmushu, R.  Disaster management takes to the skies: How new technologies are reconfiguring spatialities of power in desert locust management.  _Political Geography_ , 98:102732, 2022.
  * Gómez et al. (2018) Gómez, D., Salvador, P., Sanz, J., Casanova, C., Taratiel, D., and Casanova, J. L.  Machine learning approach to locate desert locust breeding areas based on esa cci soil moisture.  _Journal of Applied Remote Sensing_ , 12(3):036011–036011, 2018.
  * Gómez et al. (2021) Gómez, D., Salvador, P., Sanz, J., Rodrigo, J. F., Gil, J., and Casanova, J. L.  Prediction of desert locust breeding areas using machine learning methods and smos (mir_smnrt2) near real time product.  _Journal of Arid Environments_ , 194:104599, 2021.
  * Gross (2021) Gross, M.  How locusts become a plague.  _Current Biology_ , 31(10):R459–R461, 2021.  ISSN 0960-9822.  doi: https://doi.org/10.1016/j.cub.2021.05.007.  URL https://www.sciencedirect.com/science/article/pii/S0960982221006667.
  * Hochreiter & Schmidhuber (1997) Hochreiter, S. and Schmidhuber, J.  Long short-term memory.  _Neural computation_ , 9(8):1735–1780, 1997.
  * Iturbide et al. (2015) Iturbide, M., Bedia, J., Herrera, S., del Hierro, O., Pinto, M., and Gutiérrez, J. M.  A framework for species distribution modelling with improved pseudo-absence generation.  _Ecological Modelling_ , 312:166–174, 2015.  ISSN 0304-3800.  doi: https://doi.org/10.1016/j.ecolmodel.2015.05.018.  URL https://www.sciencedirect.com/science/article/pii/S030438001500215X.
  * Jakubik et al. (2023) Jakubik, J., Roy, S., Phillips, C., Fraccaro, P., Godwin, D., Zadrozny, B., Szwarcman, D., Gomes, C., Nyirjesy, G., Edwards, B., et al.  Foundation models for generalist geospatial artificial intelligence.  _arXiv preprint arXiv:2310.18660_ , 2023.
  * Keith (2001) Keith, C.  _Desert locust guidelines_.  Food and Agriculture Organization of the United Nations, 2001.  URL https://www.preventionweb.net/publication/desert-locust-guidelines.
  * Kimathi et al. (2020) Kimathi, E., Tonnang, H. E., Subramanian, S., Cressman, K., Abdel-Rahman, E. M., Tesfayohannes, M., Niassy, S., Torto, B., Dubois, T., Tanga, C. M., et al.  Prediction of breeding regions for the desert locust schistocerca gregaria in east africa.  _Scientific Reports_ , 10(1):11937, 2020.
  * Klein et al. (2022) Klein, I., van der Woude, S., Schwarzenbacher, F., Muratova, N., Slagter, B., Malakhov, D., Oppelt, N., and Kuenzer, C.  Predicting suitable breeding areas for different locust species – a multi-scale approach accounting for environmental conditions and current land cover situation.  _International Journal of Applied Earth Observation and Geoinformation_ , 107:102672, 2022.  ISSN 1569-8432.  doi: https://doi.org/10.1016/j.jag.2021.102672.  URL https://www.sciencedirect.com/science/article/pii/S0303243421003792.
  * Lee et al. (2018) Lee, H., Kim, J., and Kim, S.  A 3d convolutional neural network for anomalous propagation identification.  In _The Seventh International Conference on Intelligent Systems and Applications_ , 2018.
  * Lee et al. (2021) Lee, I., Kim, D., Wee, D., and Lee, S.  An efficient human instance-guided framework for video action recognition.  _Sensors_ , 21(24):8309, 2021.
  * Liu & Hu (2019) Liu, Z. and Hu, H.  Spatiotemporal relation networks for video action recognition.  _IEEE Access_ , 7:14969–14976, 2019.
  * Moishin et al. (2021) Moishin, M., Deo, R. C., Prasad, R., Raj, N., and Abdulla, S.  Designing deep-based learning flood forecast model with convlstm hybrid algorithm.  _IEEE Access_ , 9:50982–50993, 2021.
  * Mullié et al. (2023) Mullié, W. C., Prakash, A., Müller, A., and Lazutkaite, E.  Insecticide use against desert locust in the horn of africa 2019-2021 reveals a pressing need for change.  _Agronomy_ , 13(3), 2023.  ISSN 2073-4395.  doi: 10.3390/agronomy13030819.  URL https://www.mdpi.com/2073-4395/13/3/819.
  * Peng et al. (2020) Peng, W., Ma, N. L., Zhang, D., Zhou, Q., Yue, X., Khoo, S. C., Yang, H., Guan, R., Chen, H., Zhang, X., et al.  A review of historical and recent locust outbreaks: Links to global warming, food security and mitigation strategies.  _Environmental research_ , 191:110046, 2020.
  * Pflüger & Bräunig (2021) Pflüger, H.-J. and Bräunig, P.  One hundred years of phase polymorphism research in locusts.  _Journal of Comparative Physiology. A, Neuroethology, Sensory, Neural, and Behavioral Physiology_ , 207:321 – 326, 2021.
  * Rogers et al. (2003) Rogers, S. M., Matheson, T., Despland, E., Dodgson, T., Burrows, M., and Simpson, S. J.  Mechanosensory-induced behavioural gregarization in the desert locust schistocerca gregaria.  _Journal of Experimental Biology_ , 206(22):3991–4002, 2003.
  * Sanchez-Caballero et al. (2020) Sanchez-Caballero, A., Fuentes-Jiménez, D., and Losada-Gutiérrez, C.  Exploiting the convlstm: Human action recognition using raw depth video-based recurrent neural networks.  _arXiv preprint arXiv:2006.07744_ , 2020.
  * Shi et al. (2015) Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-c.  Convolutional lstm network: A machine learning approach for precipitation nowcasting.  _Advances in neural information processing systems_ , 28, 2015.
  * Symmons & Cressman (2001) Symmons, P. and Cressman, K.  Desert locust guidelines: biology and behaviour.  _FAO, Rome_ , pp. 1–42, 2001.
  * Tabar et al. (2021) Tabar, M., Gluck, J., Goyal, A., Jiang, F., Morr, D., Kehs, A., Lee, D., Hughes, D. P., and Yadav, A.  A plan for tackling the locust crisis in east africa: harnessing spatiotemporal deep models for locust movement forecasting.  In _Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining_, pp. 3595–3604, 2021.
  * Uvarov (1957) Uvarov, B.  The aridity factor in the ecology of locusts and grasshoppers of the old world.  In _Arid Zone Research VIII. Human and Animal Ecology. Reviews of Research_ , pp. 164–98. UNESCO, Paris, 1957.
  * Uvarov (1977) Uvarov, B.  _Grasshoppers and Locusts: A Handbook of General Acridology_ , volume 1-2.  Centre for Overseas Pest Research, London, 1977.
  * Wang et al. (2018) Wang, S., Zhang, R., Deng, Y., Chen, K., Xiao, D., Peng, P., and Jiang, T.  Discrimination of smoking status by mri based on deep learning method.  _Quantitative Imaging in Medicine and Surgery_ , 8(11):1113, 2018.
  * Wu et al. (2018) Wu, K., Shen, Y., and Wang, S.  3d convolutional neural network for regional precipitation nowcasting.  _Journal of Image and Signal Processing_ , 7(4):200–212, 2018.
  * Yusuf et al. (2022) Yusuf, I. S., ab Tessera, K., Tumiel, T., Slim, Z., Kerkeni, A., Nevo, S., and Pretorius, A.  On pseudo-absence generation and machine learning for locust breeding ground prediction in africa, 2022.