An earthquake of Mw 7.1 occurred on January 14, 2018 in the southern coast of Peru. In this study, public available Global Ionospheric Maps (GIMs) provided by the Center for Orbit Determination in Europe (CODE) were used to look for ionospheric disturbances pre-and postearthquake. Twelve days before the seismic event, a positive anomaly was detected at low latitudes in the northern hemisphere in differential vertical total electron content maps. Moreover, given the low-latitude nature of this incident, changes in the shape of the Equatorial Ionization Anomaly (EIA) were analyzed as well. A significant amplification of the northern crest in the EIA of 33.3 % was also observed 12 days before the earthquake. Because the geomagnetic and solar conditions for January 2, 2018 were very quiet and also knowing that natural ground radioactivity produced by the earthquake's preparation can increase the total electron density in the EIA, it is considered that this ionospheric disturbance is product of the earthquake's preparation. Additionally, the detection of a negative ionospheric anomaly 2 days after the incident is reported. An association to the earthquake of this negative disturbance is hinted at, due to the also the rather quiet geomagnetic and solar conditions after the seismic occurrence.

—Sugarcane diseases in Peru occur due to the agricultural community's lack of understanding of these, which means a slow response to the application of methods of control and eradication of these diseases; thus, causing economic losses and underproduction. Due to the aforementioned, a web application for sugarcane diseases recognition is proposed. The five types of sugarcane diseases that will be recognized using this system are: Pineapple Sett Rot, Ring Spot, Mosaic, Brown Rust and Leaf Scorch. This system was developed using GoogLeNet, which is a 22 layers convolutional neural network (CNN), and also the Matlab software and its App Designer extensions (for the web application creation); additionally, Matlab Web App Server was used to host the application on the web. The pre-trained neural network developed in Matlab based on the GoogLeNet architecture allowed the creation and configuration of the training parameters (supervised learning) that were evaluated, and it was considered convenient to split the data between training, validation and testing (70%, 20% and 10%, respectively). A total of 250 images composed of 50 images for each disease were used. The web application was designed in App Designer which provided us with a set of tools and a programming interface for the insertion of the trained CNN, with a validation percentage of 94.67% obtained by varying the number of epochs, reaching a maximum of 6000 iterations. Finally, the web application supported by the Matlab Web App Server was generated and tests were performed on a local network, resulting in a web application capable of identifying images within the established guidelines, with an accuracy rate of 96%

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