The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.

No abstract

No abstract

Air pollution has a wide and great influence on the concentration of constituents of the atmosphere, which leads to many effects such as acid rains and global warming. In order to avoid such unwanted adverse imbalances in the nature, designing an air pollution monitoring system (APMS) is very important. This paper discussed the development of an effective solution for monitoring the air pollution by making an Arduino-Based Air Pollution Monitoring System (APMS). Carbon monoxide and Carbon Dioxide concentration levels in air were measured and monitored using MQ4 and MQ7 gas sensors and Arduino atmega microcontroller. These sensors can detect many harmful gases and can be used for measuring their amount very accurately. The concentrations of CO and CO2 in particle per million (PPM) will be monitored and displayed on the LCD very easily. Based on these measurements the pollution level could be monitored, determined, and displayed. The experiments were carried out using the developed wireless APMS under various physical conditions. The results showed that the designed system collects reliable and reasonable real time pollution data. Three hour sampling time was executed in each location. One of the logical functions that are widely used is fuzzy logic. A fuzzy logic artificial intelligence for gas sensors is used which clarifies the presence as well as the concentration of CO and CO2 efficiently. Fuzzy logic will gives the decision about whether the air is polluted or unpolluted. This logic function we can process some existing data into a form of output which can be in the form of a status or state of action that will be performed by a tool. This proposed system will contribute in the construction of an APMS in the outdoor or even in the indoor environment.

No abstract

In recent years, driver drowsiness has been a major cause of road accidents, particularly when the driver has been driving on the highway for an extended period of time. Smart systems can now be used to prevent accidents, and a reliable driver detection system must be applied to alert the driver. In these systems, several external factors have been degrading the performance of these systems, including added noise, interference and low illumination. To overcome these limitations, this paper presents a de-noising approach for noisy images; the results show that the enhanced images improve the overall system performance and classification accuracy. The final validation accuracy is 97.5%, while the testing accuracy for S1 is 96%, S2 is 92%, and S3 is 91%. The test accuracy of S1 decreased to 45% when the Salt and pepper noise is added to the set , , when Gaussian noise is added to S2 the testing accuracy decreased to 85%, and when speckle noise is added to S3 the testing accuracy is reduced to 73%. When the median filter is used the testing accuracy for S1 become 93%, the testing accuracy for S2 increase to 91%, and the testing accuracy for S3 raises to 85%.

Artificial intelligence has been widely used in various applications such as health and safety, smart homes, greenhouses, and industrial application. It has been increasingly utilized in the industry owing to its benefits in terms of enhancing the overall performance of a given system. This study appeared from a real need in many local industries. In this paper, a prototype system has been implemented for artificial control on the temperature of the industrial panel. The paper includes two control systems executed; classical PID (Proportional Integral Derivative) and fuzzy logic with a comparison between them. Fuzzy control algorithm is developing based on Sugeno method inside PLC (Programmable Logic Controller). The connection of PLC with sensors is used by the Modbus protocol. Arduino UNO and Ethernet shield are used to connect the sensor to the router and then to PLC by Modbus.

No abstract