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.

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Laboratory chemical analysis of leaf samples can be costly and time-consuming, making it impractical for assessing crop variability. To address this challenge, researchers have focused on developing non-invasive tools that aid nitrogen (N) management, maximizing profits, minimizing environmental impact, and meeting market demands. This study aimed to develop a computer vision-based classifier system for assessing the N status in bean crops. An experiment was conducted in a greenhouse, involving five treatments (0%, 50%, 100%, 150%, and 200% N of the recommended dose) with six replications, totaling 30 pots containing six seedlings of Phaseolus vulgaris L. beans in four different phenological phases (V4, R5, R6, and R7). Digital RGB images of the bean canopies were captured using a camera at four-week intervals (30, 37, 44, and 51 days after emergence -DAE). The images were manually labeled to create an image database based on N status. Four different computational N status classifiers were developed by training a Convolutional Neural Network (CNN), one for each DAE. The classifiers were evaluated using confusion matrix metrics (accuracy, precision, and recall), resulting in an overall accuracy of about 80% when evaluating nitrogen status at five levels. Improved results were achieved by grouping the saturation classes of the 150% and 200% treatments with the 100% class (>=100% class), yielding an accuracy of 97% for 30 and 44 DAE. Promising results aside, this method opens new possibilities for improvement and application to other treatments, electromagnetic spectrum bands, and crops.

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