Today's palm trees diseases which cause a huge loss in production are extremely hard to detect either because these diseases are hidden inside the texture of the palm itself and cannot be seen by naked eyes or because it appears on its leaves which are hardly examined due to how far they really are from the ground. In this paper we're interested in detecting three of the most common diseases threatening palms today, Leaf Spots, Blight Spots and Red Palm Weevil. Diagnosis of these diseases are done by capturing normal and thermal images of palm trees then, image processing techniques were applied to the acquired images. Two classifiers were used, CNN to differentiate between Leaf Spots and Blight Spots diseases and SVM for Red Palm Weevil pest. The results for CNN and SVM algorithms showed a success rate of accuracy ratio 97.9% and 92.8% respectively, these results are considered to be the best results in this domain as far as we know. The paper also includes the first gathered thermal images dataset for palms infected with Red Palm Weevil and healthy palms as well.

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New guitarists face multiple problems when first starting out, and these mainly stem from a flood of information that they are presented with. Students also typically struggle with proper pitch frequency recognition and accurate left-hand motion. A variety of relevant solutions have been suggested in the existing literature; however, the majority have ultimately settled on two approaches. The first is finger motion capture, wherein researchers focus on extracting finger positions through analyzing images and videos. The second is note frequency recognition, wherein researchers focus on analyzing notes and frequencies from audio recordings. This paper proposes a novel hybrid solution that includes both finger motion capture and note frequency recognition in order to conduct a full assessment and give feedback on a guitarist’s performance. To classify hand positions, several classification algorithms are tested. The random forest algorithm obtained superior results, with an accuracy of 99% for overall hand movement and an average of 97.5% for the classification of each finger. Meanwhile, two algorithms were tested for note recognition, where the harmonic product spectrum (HPS) approach obtained the highest accuracy of 95%.

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