## 1 Introduction  
The desert locust (_Schistocerca gregaria_ sp.) is considered one of the most dangerous migratory pest species in the world [1, 2], due to its fast reproduction time, capability of adaptation to and survival in difficult conditions [3], ability to migrate long distances and its harmful impact on crops [4]. Specifically, plagues of desert locusts have swept the Western Africa region for centuries [5], with high impacts in terms of crops losses and food scarcity, on the environment (due to pesticides and treatments against these insects [6]) and finally on the economy of the affected countries [7]. Countries such as Mauritania, Burkina Faso, Mali or Niger have suffered devastating desert locust plagues in recent decades, such as those between winter 2003 and spring 2004 in this zone [1]. The Food and Agriculture Organization of the United Nations (FAO) [8] has pointed out the necessity of implementing preventive strategies in the main affected countries. Western and Eastern Africa [5, 9], Southwest Asia [10] and the area around the Red Sea are the zones with favourable climatic conditions to witness an upsurge of desert locust plaques [1]. These preventive strategies include the detection of breeding ground areas, the tracking of locust intrusion with Internet of Things devices [11] and the treatment with pesticides whenever possible, in order to reduce the probability of plagues in a given region [12]. There are many previous studies on the detection of desert locust breeding ground areas, using different data (satellite [5; 13; 14] and ground measurements [15]) and including modern computational techniques [16]. In recent years, the use of machine learning (ML) algorithms has been very important in Earth observation problems [17; 18; 19]. In ref. [20], a random forest algorithm is applied to the problem of locating desert locust juvenile individuals in Mauritania using soil moisture data. In ref. [21], a rule generation algorithm by means of genetic algorithms is proposed for the problem of predicting desert locust presence (breeding area locations). In ref. [22], a Hidden Markov Model is applied to predict the severity of locust plagues in croplands using remote sensing data. In ref. [23], different ML classifiers such as the gradient boosting, RF and Maximum Entropy methods are compared in a problem of desert locust detection in different countries of Western Africa, such as Niger, Mauritania, Mali, Algeria and Morocco. The results obtained have shown good results when the database are completed with \"pseudo-absenses\" data. In ref. [24], different ML classification algorithms have been applied to the problem of desert locus breeding area prediction across Africa and Asia. Specifically, eXtreme gradient boosting, Weighted k-Nearest Neighbors, Feed-Forward Neural Networks, Support Vector Machines and RF algorithms have been applied and discussed in different breeding area locations, including pseudo-absence data. In ref. [25], the problem of desert locust presence prediction using an ML classification approach (Support Vector Machines with sliding windows) and remote sensing data is tackled. The region of with pseudo-absenses is considered. In ref. [26], a deep learning approach including convolutional neural networks and LSTM networks is presented for the problem of desert locust detection (classification task) in East Africa. 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 septem/asciata_), 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. All these previous approaches dealing with ML algorithms for desert locust prediction and related problems share some common features.