This paper presents the fusion of swarm behavior in multi robotic system specifically the quadrotors unmanned aerial vehicle (QUAV) operations. This study directed on using robot swarms because of its key feature of decentralized processing amongst its member. This characteristic leads to advantages of robot operations because an individual robot failure will not affect the group performance. The algorithm emulating the animal or insect swarm behaviors is presented in this paper and implemented into an artificial robotic agent (QUAV) in computer simulations. The simulation results concluded that for increasing number of QUAV the aggregation accuracy increases with an accuracy of 90.62%. The experiment for foraging revealed that the number of QUAV does not affect the accuracy of the swarm instead the iterations needed are greatly improved with an average of 160.53 iterations from 50 to 500 QUAV. For swarm tracking, the average accuracy is 89.23%. The accuracy of the swarm formation is 84.65%. These results clearly defined that the swarm system is accurate enough to perform the tasks and robust in any QUAV number.

Irrigation system plays a very important role in organic farming. It provides the necessary water requirement for the whole farm. But it is necessary for the irrigation system to be efficient, especially in providing the optimal distribution of available water resources. Also, since the water from an irrigation system will come from an underground reservoir, it requires electrical water pump to collect water. In a smart farm, proper allocation of resources is required. In order to control the usage of water resources for irrigation, this paper proposes the design of an automated organic irrigation system in controlling and properly allocating the available water resources for the irrigation system and available electricity for the use of the pump. Experiments through MATLAB simulation were done using a proposed system to achieve the optimum water and electrical resource distribution.

The paper presents an intelligent system architecture for detecting traffic violations based on vision. This study aims to better manage traffic conditions in block intersections by employing a computer vision system that facilitates the identification of traffic violations committed in the road intersection. The architecture includes three sub-system: video capture sub-system, intelligent operating architecture (IOA) sub-system, and output sub-system. The IOA manages different algorithms to recognize traffic violations. The algorithms developed are vehicle detection and tracking, plate number localization, plate character recognition, and traffic violations identification. The traffic violations addressed in this study are number coding, over-speeding, and swerving. The research study is in the initial phase of development, and the experiment results showed that optical character recognition have 86.11% accuracy and speed measurement have 88.45% accuracy.

Blocked intersections have been a contributing factor in the city-wide traffic congestion, especially in metropolitan cities. This research study aims to develop a better traffic violations management system in city-road intersections by using a machine vision system that automatically identifies and tags traffic violations committed in an intersection. The proposed system have three main sub-systems which are the video capture, video analysis, and output sub-systems. This study presents the development and results of a vehicle detection and tracking system using corner feature point detection and artificial neural networks for the vision-based contactless traffic violations apprehension system. This detection and tracking system serves as the front-end processing in the video analysis sub-system. Experiments were conducted for different corner feature-points detection algorithm: Harris, Shi-Tomasi, and Features from Accelerated Segment Test (FAST). The results showed that in the testing phase Harris-ANN have 89.09% accuracy, Shi-Tomasi-ANN have 88.48%, and FAST-ANN have 90.30% accuracy.

In road traffic scene analysis, it is important to observe vehicular traffic and how pedestrian foot traffic affects the over-all traffic situation. Road context is also significant in proper detection of vehicles and pedestrians. This paper presents a vehicle-pedestrian detection and classification system with road context recognition using convolutional neural networks. Using Catch-All traffic video data sets, the system was trained to identify vehicles and pedestrians in four different road conditions such as low-altitude view T-type road intersection (DSO), mid-altitude view bus stop area in day-time (DS4-1) and night-time (DS4-3) condition, and high-altitude view wide intersection (DS3-1). In the road context recognition, the system was first tasked to identify in which of the four road conditions the current traffic scene belongs. This is designed to ensure a high detection rate of vehicles and pedestrians in the mentioned road conditions. Road context recognition has 98.64% training accuracy with 2800 sample images, and 100% validation accuracy with 1200 sample images. After road context recognition, a detection algorithm for vehicle and pedestrians was trained for each condition. In DSO, the training accuracy is 97.75% with 1200 image samples, while validation accuracy is 94.75% with 400 image samples. In DS3-1, the training accuracy is 98.63% with 1400 image samples, while validation accuracy is 98.29% with 600 image samples. In DS4-1, the training accuracy is 99.43% with 1400 image samples, while validation accuracy is 99.83% with 600 image samples. In DS4-3, the training accuracy is 97.77% with 1400 image samples, while validation accuracy is 98.29% with 600 image samples.

In this paper, a fuzzy logic controller was design and develop to control the temperature, relative humidity and Carbon Dioxide (CO₂) inside the prototype tomato growth chamber. The model was develop to automatically adjust the inside parameters to obtain the optimum tomato plant environment condition. The growth chamber fuzzy logic controller was modeled using the MATLAB fuzzy logic tool box. In this research we design a fuzzy logic controller (FLC) to control the environment parameters in the growth chamber. In order to provide the most suitable conditions for the growth of the tomato plant and might minimize energy consumption.