Recently, intelligent video surveillance technologies using unmanned aerial vehicles (UAVs) have been considerably increased in the transportation sector. Real time collection of traffic videos by the use of UAVs finds useful to monitor the traffic flow and road conditions. Since traffic jams have become common in urban areas, it is needed to design artificial intelligence (AI) based recognition techniques to attain effective traffic flow monitoring. Besides, the traffic flow monitoring system can assist the traffic managers to start efficient dispersal actions. Therefore, this study designs a real time traffic flow monitoring system using deep learning (DL) and UAVs, called RTTFM-DL. The proposed RTTFM-DL technique aims to detect vehicles, count vehicles, estimate speed and determine traffic flow. In addition, an efficient vehicle detection model is proposed by the use of Faster Regional Convolutional Neural Network (Faster RCNN) with Residual Network (ResNet). Also, a detection line based vehicle counting approach is designed, which is based on overlap ratio. Finally, traffic flow monitoring takes place based on the estimated vehicle count and vehicle speed. In order to guarantee the effectual performance of the RTTFM-DL technique, a series of experimental analyses take place and the results are examined under varying aspects. The experimental outcomes highlighted the betterment of the RTTFM-DL technique over the recent techniques. The RTTFM-DL technique has gained improved outcomes with a higher accuracy of 0.975.

Crop diseases is a major risk to food, but crop disease identification is difficult due to lack of infrastructure. Detecting and Identifying disease from the plants images is one of the interesting research areas in computer and agriculture field. The main Goal of the project is to identify the diseases in the plant leaves and to prevent them in the early stage. This Plant disease detection model is totally based on the leaf of the plants with image classification, developed using CNN (Convolutional Neural Network) algorithm. The developed model trained from scratch to identify the disease and it is able to recognize different types of plant diseases. The reason for choosing CNN is Classifier algorithm mainly extracts more features from the image datasets rather than other classifier algorithms. The trained model achieves an accuracy of 99.4%. The major threat in food infections are yield infections, but proving them is difficult due to a lack of evidence. This study provides an overview of various image processing and machine learning algorithms for identifying plant disease from photos of diseased plants. The CNN classifier algorithm is a powerful tool for diagnosing and predicting the infections which extracts more features from the image dataset. Our goal is to assist farmers by recognizing the diseases in the initial plant stage.