Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines ...

Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines ...

El cultivo de quinua es muy susceptible a la competencia por las malezas en sus primeros estados fenológicos, para tal efecto se realizó este trabajo de investigación en el distrito de Sicaya, durante la campaña agrícola 2012 – 2013; los objetivos fueron: a) determinar el porcentaje de control, en un área de 1 m² con el uso de herbicidas. b) determinar el efecto fitotóxico que causan los herbicidas Pendimetalin (Tanke 40EC) 2L/200L e Imazethapyr (Petardo 10.6 SL)1L/200L en el cultivo de quinua. Para lo cual se utilizó el diseño de bloques completos al azar, con 12 tratamientos y 3 repeticiones. Las variables fueron: Número de malezas en 1m², control de malezas, efecto fitotóxico, componentes de rendimiento y rentabilidad. Los resultados fueron: Los herbicidas Tanke y Petardo controlaron eficientemente las malezas registradas, donde se obtuvieron para yuyo blanco, yuyo amar...