The unmanned surface vehicles have been used most frequently in recent years in different applications, like environmental research. For this vehicles to accomplish their autonomous missions, a path-following algorithm is necessary to reduce the cross-track error in the presence of environmental disturbance. This article presents a control scheme based on the path-following nonlinear guidance law for a small unmanned surface vehicle called Krick Felix which follows a straight path. A dynamic model of 3 degrees of freedom for this vehicle is presented. The control scheme consists of a cascade control loop that is capable of guaranteeing zero cross-track error in the presence of environmental disturbance without adding an integral action. A nonlinear Lyapunov stability analysis is carried out for this control scheme taking in consideration the dynamics of both the inner loop and the external loop. The simulation was realized by implementing the 3-degree-of-freedom nonlinear model of the Krick Felix. The simulation also took account of the environmental factors, that is, marine currents. An experimental test is carried out with the Krick Felix where the control scheme present satisfactory results.
Precision agriculture, making use of the spatial and temporal variability of cultivable land, allows farmers to refine fertilization, control field irrigation, estimate planting productivity, and detect pests and disease in crops. To that end, this paper identifies the spectral reflectance signature of brown rust (Puccinia melanocephala) and orange rust (Puccinia kuehnii), which contaminate sugar cane leaves (Saccharum spp.). By means of spectrometry, the mean values and standard deviations of the spectral reflectance signature are obtained for five levels of contamination of the leaves in each type of rust, observing the greatest differences between healthy and diseased leaves in the red (R) and near infrared (NIR) bands. With the results obtained, a multispectral camera was used to obtain images of the leaves and calculate the Normalized Difference Vegetation Index (NDVI). The results identified the presence of both plagues by differentiating healthy from contaminated leaves through the index value with an average difference of 11.9% for brown rust and 9.9% for orange rust.
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