In surveillance and monitoring systems, the use of mobile vehicles or unmanned aerial vehicles (UAVs), like the drone type, provides advantages in terms of access to the environment with enhanced range, maneuverability, and safety due to the ability to move omnidirectionally to explore, identify, and perform some security tasks. These activities must be performed autonomously by capturing data from the environment; usually, the data present errors and uncertainties that impact the recognition and resolution in the detection and identification of objects. The resolution in the acquisition of data can be improved by integrating data sensor fusion systems to measure the same physical phenomenon from two or more sensors by retrieving information simultaneously. This paper uses the constant turn and rate velocity (CTRV) kinematic model of a drone but includes the angular velocity not considered in previous works as a complementary alternative in Lidar and Radar data sensor fusion retrieved using UAVs and applying the extended Kalman filter (EKF) for the detection of moving targets. The performance of the EKF is evaluated by using a dataset that jointly includes position data captured from a LiDAR and a Radar sensor for an object in movement following a trajectory with sudden changes. Additive white Gaussian noise is then introduced into the data to degrade the data. Then, the root mean square error (RMSE) versus the increase in noise power is evaluated, and the results show an improvement of 0.4 for object detection over other conventional kinematic models that do not consider significant trajectory changes.

This article presents the application of supervised learning and image classification for the early detection of late blight disease in potato using convolutional neural network and support vector machine SVM. The study was realized in the Boyacá department, Colombia. An initial dataset is created with the acquisition of a large number of images directly from the crops. These images are pre-processed in order to extract the main characteristics of the late blight disease. A classification model is developed to identify the potato plants as healthy or infected. Several performance, efficiency, and quality metrics were applied in the learning and classification tasks to determine the best machine learning algorithms. Then, an additional data set was used for validation, image classification, and detection of late blight disease in potato crops in the department of Boyacá, Colombia. The results obtained in the AUC curve show that the CNN trained with the data set obtained an AUC equal to 0.97; and the analysis through SVM obtained an AUC equal to 0.87. Future work requires the development of a mobile application with advanced features as a technological tool for precision agriculture that supports farmers with increased agricultural productivity.

This paper reports the design of a deep neural network (DNN) focused to predict patterns of student dropout at the UPTC University of Colombia. We apply specialized artificial intelligence (AI) algorithms for the implementation of the DNN specifically were using a learning machine for classification and clustering task. Additionally, a dataset containing 17 attributes of 3000 academically active students was structured; the data set has been prepared to be trained as an entry to the neural network. The final result of the research reveals a predictive model trained through the DNN validated by several quality metrics that demonstrate reliability and accuracy of the results accomplished through the model.

La presencia del tizón tardío o gota en el cultivo de papa afecta directamente el crecimiento de la planta y el desarrollo del tubérculo, por ello, es importante la detección temprana de la enfermedad. Actualmente, la aplicación de redes neuronales convolucionales es una oportunidad orientada a la identificación de patrones en la agricultura de precisión, incluyendo el estudio del tizón tardío, en el cultivo de papa. Este estudio describe un modelo de aprendizaje profundo capaz de reconocer el tizón tardío en el cultivo de papa, por medio de la clasificación de imágenes de las hojas. Se utilizó, en la aplicación de este modelo, el conjunto de datos aumentado de PlantVillage, para entrenamiento. El modelo propuesto ha sido evaluado a partir de métricas de rendimiento, como precisión, sensibilidad, puntaje F1 y exactitud. Para verificar la efectividad del modelo en la identificación y la clasificación del tizón tardío y comparado en rendimiento con arquitecturas. como AlexNet, ZFNet, VGG16 y VGG19. Los resultados experimentales obtenidos con el conjunto de datos seleccionado mostraron que el modelo propuesto alcanza una exactitud del 90 % y un puntaje F1, del 91 %. Por lo anterior, se concluye que el modelo propuesto es una herramienta útil para los agricultores en la identificación del tizón tardío y escalable a plataformas móviles, por la cantidad de parámetros que lo comprenden.

Global climate change primarily affects the spatiotemporal variation in physical quantities, such as relative humidity, atmospheric pressure, ambient temperature, and, notably, precipitation levels. Accurate precipitation predictions remain elusive, necessitating tools for detailed spatiotemporal analysis to better understand climate impacts on the environment, agriculture, and society. This study compared three learning models, the autoregressive integrated moving average (ARIMA), random forest regression (RF-R), and the long short-term memory neural network (LSTM-NN), using monthly precipitation data (in millimeters) from 757 locations in Boyacá, Colombia. The inputs for these models were based on satellite images obtained from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. The LSTM-NN model outperformed others, precisely replicating precipitation observations in both training and testing datasets, significantly reducing the root mean square error (RMSE), with average monthly deviations of approximately 19 mm per location. Evaluation metrics (RMSE, MAE, R², MSE) underscored the LSTM model’s robustness and accuracy in capturing precipitation patterns. Consequently, the LSTM model was chosen to predict precipitation over a 16-month period starting from August 2023, offering a reliable tool for future meteorological forecasting and planning in the region.

Introduction. Automatic detection can be useful in the search of large crop fields by simply detecting the disease with the symptoms appearing on the leaf. Objective: This paper presents the application of machine learning techniques aimed at detecting late blight disease using unsupervised learning methods such as K-Means and hierarchical clustering. Method: The methodology used is composed by the following phases: acquisition of the dataset, image processing, feature extraction, feature selection, implementation of the learning model, performance measurement of the algorithm, finally a 68.24% hit rate was obtained being this the best result of the unsupervised learning algorithms implemented, using 3 clusters for clustering. Results: According to the results obtained, the performance of the K-Means algorithm can be evaluated, i.e. 202 hits and 116 misses. Conclusions: Unsupervised learning algorithms are very efficient when processing a large amount of data, in this case a large amount of images without the need for predefined labels, its use to solve local problems such as late blight affectations in potato crops are novel,