Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time and increases efficiency in agricultural management activities, which improves the food industry. Agricultural mapping is necessary for resource management and requires technologies for farming challenges. The mapping in agricultural AI applications gives efficiency in mapping and its subsequent use in decision-making. This study analyses AI’s current state in agricultural mapping through bibliometric indicators and a literature review to identify methods, agricultural resources, geomatic tools, mapping types, and their applications in agricultural management. The methodology begins with a bibliographic search in Scopus and the Web of Science (WoS). Subsequently, a bibliographic data analysis and literature review establish the scientific contribution, collaboration, AI methods, and trends. The United States (USA), Spain, and Italy are countries that produce and collaborate more in this area of knowledge. Of the studies, 76% use machine learning (ML) and 24% use deep learning (DL) for agricultural mapping applications. Prevailing algorithms such as Random Forest (RF), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs) correlate mapping activities in agricultural management. In addition, AI contributes to agricultural mapping in activities associated with production, disease detection, crop classification, rural planning, forest dynamics, and irrigation system improvements.

New challenges arise with the modernization of agricultural practices, like the concept of environmental and economic sustainability of the production process. The profitability of a livestock farm depends on the quality of the food supplied to the animals, which if not managed in a technical way may affect the profitability of production. Demographic growth in Ecuador requires that more areas be allocated annually to food production for this population, which is why, in order to prevent these changes in land use, it seeks to implement new technologies for optimization. Therefore, we analyzed the variation of NDVI between a geostatistical model generated with data obtained from the FieldSpec4 terrestrial spectroradiometer and multispectral data obtained by the Parrot Sequoia sensor on a UAV in pastures prior to grazing. Therefore, four methodological phases have been performed, which have been used to validate the sensors in the laboratory, to calculate the NDVI model by UAV, to calculate a geostatistical model of NDVI and to analyze the correlation of both models. Where the correlation analysis between these NDVI models yields a positive correlation with a value of 76.89% correlation in addition to a value of R ² = 0.591, defined a positive adjustment goodness. The obtained NDVI model with the Parrot Sequoia sensor has been the one that best fits reality, in addition to its use in UAV́s has greatly reduced costs respect to the conventional agriculture.

Global food security faces a substantial risk stemming from water scarcity, particularly in regions heavily dependent on rainfall for agricultural purposes. In the Andean region, which is grappling with water scarcity, innovative and sustainable approaches are imperative for securing food sources. Plastic mulching has emerged as a potential solution to address water scarcity challenges by conserving soil moisture and optimizing growing conditions. A two-year field experiment was conducted that evaluated the efficacy of plastic mulching in preserving soil water content and reducing drought stress for maize. Two maize cultivars were grown with plastic mulching and conventional practices using a randomized completed block design with three replicates. The results demonstrated that employing plastic mulch led to a rise in the soil water content, particularly within the initial 30 cm of depth, resulting in variances of up to 4.71% humidity between the 20 and 30 cm depths. For the 2019 and 2020 evaluation years, plastic mulching elevated the volumetric water content in the first 30 cm of the soil by 8.39% and 14.18%, respectively. Additionally, it elevated the soil temperature by 1.26 °C and reduced the maize flowering time. Plastic mulching substantially enhanced the fresh corn yield, reaching a 162% increase compared to conventional plots. These findings underscore the potential benefits of plastic mulching in conserving the soil water content, alleviating drought and cold stress for crops in the Andean region.

La investigación evaluó el comportamiento durante el desarrollo del cultivar Dominico Hartón (Musa spp.) de 14 semanas de edad, bajo escenarios simulados de estrés hídrico por sequía e inundación. El trabajo se desarrolló en la Ciudad de la Investigación, Innovación y Desarrollo Agropecuario (CIIDEA) de la Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López (ESPAM-MFL), ubicada en la parroquia Calceta, cantón Bolívar, provincia de Manabí. Se implementaron tres tipos de tratamientos: T1: testigo sin estrés hídrico, T2: condiciones de sequía, T3: condiciones de inundación. Entre los resultados obtenidos, la biomasa seca de la raíz (p<0,001), donde el tratamiento sometido a condiciones de inundación (T3) incrementó en un 42% en comparación al manejo sin estrés hídrico (T1). Para la variable de biomasa seca del pseudotallo (P<0,001) ocurrió algo similar, donde T3 alcanzó un 102% mayor biomasa seca en el pseudotallo con relación a T1. Para la variable de biomasa seca de la hoja (P<0,001), el T1 y T3 tuvieron valores similares (≈28 g), pero con relación al tratamiento sometido a sequía (T2), este presentó una biomasa de 59% menor a la media de los otros tratamientos. Estos hallazgos destacan que las diferentes condiciones de sequía e inundación afecta de manera significativa el crecimiento y desarrollo de las plántulas de plátano.