Development and evaluation of a prototype precision spot spray system using image analysis to target guinea grass in sugarcane

Herbicide usage in weed control represents a significant economic cost and environmental risk in Australian sugarcane production. Weed spot spraying has potential to increase sugarcane production whilst reducing chemical usage and environmentally damaging runoff. However, weed spot spraying is traditionally a laborious manual task. This paper reports on a precision machine vision system that was developed to automatically identify and target the difficult to control weed Panicum spp. (Guinea Grass) in sugarcane crops. The infield machine vision system comprised a camera and artificial illumination to enable day and night trials. Image analysis algorithms were developed to discriminate Guinea Grass and sugarcane based on colour and textural differences between the species. A positive weed identification from the image analysis activated solenoid-controlled spray nozzles. Evaluations of the system in a sugarcane crop established that the image analysis algorithm parameters required frequent recalibration during the day but that the requirement for recalibration was reduced at night with constant artificial illumination. The algorithm was only effective at detecting mature Guinea Grass. The developed technology is considered a viable alternative to manual spot spraying of mature Guinea Grass in sugarcane at night. A cost benefit analysis of the new weed control system indicated potential grower savings of $170/ha by adopting the technology

Visual Servo Controllers for an UAV Tracking Vegetal Paths

In the inspection and data collection of large areas as crop fields, where an aerial vehicle should follow an objects line accurately, autonomous flight is adesirable featurewith unmanned aerial vehicles (UAVs). To attain this objective, three visual servo controllers are proposed, one of them is position based and the other two are image based using inverse Jacobian and concepts of passivity respectively. All controllers are developed based on the kinematic model of the vehicle, and a dynamic compensation is designed to be added in cascade with the kinematic one. The performance of the control systems are compared through simulation results. The main contribution is the development of the image based controller using passivity properties of the system, the stability and robustness analysis and the comparative performance with other controllers when used for an UAV following vegetal lines. These comparative results are valuable to choose the appropriate driver for a specific application.Fil: Sarapura, Jorge Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Roberti, Flavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Toibero, Juan Marcos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Sebastián, José Maria. Universidad Politécnica de Madrid; EspañaFil: Ricardo Carelli. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Atypical Facial Landmark Localisation with Stacked Hourglass Networks:A Study on 3D Facial Modelling for Medical Diagnosis

While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investigate the facial landmark detection for atypical 3D facial modelling in facial palsy cases, while potentially such modelling can assist the medical diagnosis using atypical facial features. In our work, a study of landmarks localisation methods such as stacked hourglass networks is conducted and evaluated to ascertain their accuracy when presented with unseen atypical faces. The evaluation highlights that the state-of-the-art stacked hourglass architecture outperforms other traditional methods