A face recognition system for automatic door access control has been developed in this work with a view to providing a relatively more robust and foolproof access control which can provide better security and reduce human errors inherent in other conventional methods. The system was designed with machine learning and artificial intelligence to capture faces, train faces with machine mode, and run trained faces to grant access to the user. The system uses the RaspberryPi module, camera module, servo motor and the GSM module which were all incorporated into the fabricated building to make up the prototype developed to provide access control by means of facial biometrics. In order to grant access to registered users, various photos of the users were taken in different positions and expressions with proper illumination. The user’s face is been captured by the camera module and saved in the database with the help of Raspberry Pi Module. Good lighting condition and other favorable conditions helps the camera module to recognize faces and sends signal to the Raspberry Pi which processes these images and opens the door with the help of the servo motor. The developed prototype was used to train fifty (50) users. It granted access to all fifty (50) users when there was proper illumination and pose but five (5) and nine (9) users respectively were denied access due to challenges of poor illumination and pose variation.

A functional smart irrigation system with integrated GSM and Wi-Fi features that enables the farmer to remotely check soil moisture status as well as turn on and turn off the irrigation pump remotely, where necessary had been developed in this study. The system consists of a DC pump, a GSM module, moisture sensor, and the NodeMCU microcontroller. The prototype is essentially a mix of hardware module and software program and made up of several subunits. It behaves functions intelligently as a switching system being able to detect the moisture content level of the soil and then automatically irrigate the crop where the conditions are necessary. The motor is automatically turned ON or OFF in accordance with the soil moisture content level. The readings of the soil sensor are sent to a processing unit which generates graphs for analysis thereby helping the farmer make informed decisions.

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