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Nowadays, according to the Food and Agriculture Organization (FAO), agricultural pests cause severe loss of global crop production every year. In order to eliminate these harmful insect pests, smart agriculture is the best way for farmers to use artificial intelligence techniques integrated with modern information and communication technology. Thus, they can increase the productivity of their crops. The main limiting factor for cultivation, however, is pest and disease infections. It is not possible to eradicate these diseases, but they can be controlled and monitored to minimize their impact. Crop diseases can be rapidly diagnosed by using automated image identification systems. An accurate identification system requires the extraction of features from images. To identify the samples, we used three different classes of machine learning: a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN). In addition, our deep learning method outperforms other previous recognition methods, namely Single Shot Multi-Box Detector (SSD), which increases the accuracy and multi-directional way of pest detection. Our results contribute to the use of machine learning in agriculture literature and have implications for research in deep learning and practical implications. [ABSTRACT FROM AUTHOR]

The age of technology has introduced integrated wearables into the area of driving to prevent accidents. A smart device is fitted easily into any moving vehicle, which renders a convenient option regarding the activities of the drivers such as abnormality, health condition, and sleep information. This research study discusses two types of outcomes that are enabled according to the situations of the input provided to the BPNN (Back Propagation Neural Network). At first, when an anomaly is predicted within the vehicle, such as the unusual behaviour of the driver, then it is reported through the help of IoT sensors installed inside the vehicle. The BPNN communicates the relevant signal to the driver assistance system to gain control over the vehicle and safely stop it at an appropriate place on the road. The second type of outcome discussed is when an anomaly is explored on the road when a vehicle moves in a heedless way. In such cases, the nearby vehicles are alerted and in the case of an accident are reported, the signals are sent to the local police officials. These suggested instructions perhaps minimize the damage and decrease the rate of mortality every year because of road accidents. Appropriate actions are taken by the model in averting the occurrence of accidents using cloud AI. The performance of the proposed system is assessed based on its accuracy rate. [ABSTRACT FROM AUTHOR]