2 research outputs found
Segmentación de suelos de acuerdo con sus características fisicoquímicas a través modelos de aprendizaje automático
La segmentación de la calidad fisicoquímica del suelo permite establecer zonas que requieren manejos similares o zonas con vulnerabilidades en las que se deben enfocar estrategias para su conservación y/o recuperación; en este sentido, se tomaron 1139 muestras de suelos en predios en la zona rural de los municipios de Córdoba, Cuaspud, Iles, Ipiales y Potosí del departamento de Nariño a las que se les realizó análisis fisicoquímicos (contenidos de arenas, limos y arcillas, pH, conductividad eléctrica, contenido de materia orgánica, nitrógeno, fosforo intercambiable, azufre, calcio, magnesio, potasio, capacidad de intercambio catiónico efectiva, aluminio, hierro, manganeso, cobre, zinc, boro, saturación de aluminio, saturación de magnesio, saturación de potasio, saturación de calcio, relación calcio y magnesio, relación calcio y potasio, relación magnesio y potasio, relación calcio, magnesio y potasio); para establecer como se podrían segmentar estas muestras, inicialmente se realizó una correlación de Pearson para conocer relaciones lineales entre variables, para luego implementar un análisis de componentes principales (PCA); con esta información se aplicó varios modelos de aprendizaje no supervisado para determinar el número óptimo de clusters en los que segmentar la información; posteriormente, se decidió realizar un modelo supervisado, Random Forest (RF), teniendo en cuenta la información del PCA y de clusters, para determinar las variables originales con mayor importancia relativa en el agrupamiento de la información; finalmente se logró establecer las variables y los valores de estas que permitían el agrupamiento aplicando el modelo Decision Tree (DT); en este sentido, se logró establecer que la mejor forma de segmentar la información de las muestras de suelo es a través de tres clusters, y que las variables que mayor peso tienen en la generación de estos grupos son los contenidos de Arena y Limo, la relación Ca/Mg/K y la relación Ca/K, denotando diferencias principalmente entre suelos Franco arenosos y franco.The segmentation of the physicochemical quality of the soil makes it possible to establish areas that require similar management or areas with vulnerabilities in which strategies for their conservation and/or recovery should be focused; In this sense, 1139 soil samples were taken from properties in the rural area of the municipalities of Córdoba, Cuaspud, Iles, Ipiales and Potosí in the department of Nariño, on which physicochemical analyzes were carried out (contents of sand, silt and clay, pH, electrical conductivity, organic matter content, nitrogen, exchangeable phosphorus, sulfur, calcium, magnesium, potassium, effective cation exchange capacity, aluminum, iron, manganese, copper, zinc, boron, aluminum saturation, magnesium saturation, saturation potassium, calcium saturation, calcium and magnesium ratio, calcium and potassium ratio, magnesium and potassium ratio, calcium, magnesium and potassium ratio); To establish how these samples could be segmented, a Pearson correlation was initially performed to determine linear relationships between variables, and then a principal component analysis (PCA) was implemented; With this information, several unsupervised learning models were applied to determine the optimal number of clusters in which to segment the information; subsequently, it was decided to carry out a supervised model, Random Forest (RF), taking into account the information from the PCA and clusters, to determine the original variables with greater relative importance in the grouping of information; finally, it was possible to establish the variables and their values that allowed the grouping by applying the Decision Tree (DT) model; In this sense, it was possible to establish that the best way to segment the information of the soil samples is through three clusters, and that the variables that have the greatest weight in the generation of these groups are the contents of Sand and Silt, the Ca/Mg/K ratio and the Ca/K ratio, denoting differences mainly between sandy loam and loam soils
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Drones: Innovative Technology for Use in Precision Pest Management.
Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers