The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.

No abstract

No abstract

No abstract

No abstract

No abstract

No abstract

Laser sensor applications associated with LiDAR (Light Detection and Ranging) technology on platforms allow the evaluation of crop and forest biomass in a non-invasive way. This study presents the development of a measurement system based on LiDAR technology aimed at the proposed assessment of the height of sugarcane plants during the pre-harvest period. A laser sensor, a GNSS (Global Navigation Satellite System) receiver, and an inertial unit compose the measurement system. The equipment was integrated with a computer for data acquisition and installed on an agricultural tractor platform. The GNSS receiver with a real-time kinematic signal was synchronized to the laser sensor to obtain the point cloud and to the inertial unit to evaluate the intensity of the vibration from platform oscillation. Data acquisition was carried about 10 days before sugarcane harvest. The developed steps of acquisition and data processing enabled the generation of point clouds with a density of about 2,000 points m − ² and the extraction of metrics related to the height of sugarcane plants. The influence of vibration in the dataset was more significant in one of the experimental fields due the high-amplitude spectral power observed.