3 research outputs found
Smart Palm: An IoT Framework for Red Palm Weevil Early Detection
Smart agriculture is an evolving trend in agriculture industry, where sensors
are embedded into plants to collect vital data and help in decision making to
ensure higher quality of crops and prevent pests, disease, and other possible
threats. In Saudi Arabia, growing palms is the most important agricultural
activity, and there is an increasing need to leverage smart agriculture
technology to improve the production of dates and prevent diseases. One of the
most critical diseases of palms if the red palm weevil, which is an insect that
causes a lot of damage to palm trees and can devast large areas of palm trees.
The most challenging problem is that the effect of the weevil is not visible by
humans until the palm reaches an advanced infestation state. For this reason,
there is a need to use advanced technology for early detection and prevention
of infestation propagation. In this project, we have developed am IoT based
smart palm monitoring prototype as a proof-of-concept that (1) allows to
monitor palms remotely using smart agriculture sensors, (2) contribute to the
early detection of red palm weevil. Users can use web/mobile application to
interact with their palm farms and help them in getting early detection of
possible infestations. We used Elm company IoT platform to interface between
the sensor layer and the user layer. In addition, we have collected data using
accelerometer sensors and we applied signal processing and statistical
techniques to analyze collected data and determine a fingerprint of the
infestation
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning
In the Muslim community, the prayer (i.e. Salat) is the second pillar of Islam, and it is the most essential and fundamental worshiping activity that believers have to perform five times a day. From a gestures' perspective, there are predefined human postures that must be performed in a precise manner. However, for several people, these postures are not correctly performed, due to being new to Salat or even having learned prayers in an incorrect manner. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an artificial intelligence assistive framework that guides worshippers to evaluate the correctness of the postures of their prayers. This paper represents the first step to achieve this objective and addresses the problem of the recognition of the basic gestures of Islamic prayer using Convolutional Neural Networks (CNN). The contribution of this paper lies in building a dataset for the basic Salat positions, and train a YOLOv3 neural network for the recognition of the gestures. Experimental results demonstrate that the mean average precision attains 85% for a training dataset of 764 images of the different postures. To the best of our knowledge, this is the first work that addresses human activity recognition of Salat using deep learning.info:eu-repo/semantics/publishedVersio