Crop diseases constitute a big threat to plant existence, but their rapid identification remains difficult in many parts of the planet because of the shortage of the required infrastructure. In computer vision, plant leaf detection made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. employing a public dataset of 4,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to spot one crop species and 4 diseases (or absence thereof). The trained model achieves an accuracy of 97.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of coaching deep learning models on increasingly large and publicly available image datasets presents a transparent path toward smartphoneassisted crop disease diagnosis on a large global scale. After the disease is successfully predicted with a decent confidence level, the corresponding remedy for the disease present is displayed that may be taken as a cure.
The technology of detection within the captured video has implementation within the sort of fields. This emerging technology when implemented over the real-time video feeds could even be beneficial. The supreme good thing about vehicle detection within the real-time streaming video feed is to trace vehicles in busy roads or Bridges like Padma or Jamuna Bridge. An accidents occurred anywhere which may rather be detected. Vehicle detection also called computer vision beholding, basically the scientific methods and ways of how machines see instead of human eyes. This chapter aims to explore the prevailing challenging issue within the planet of unsupervised surveillance and security, Helps traffic police, Maintaining records and Traffic surveillance control. The detection of vehicles is implemented with enhanced algorithms and machine learning libraries like OpenCV, TensorFlow, and others. The varied approaches are accustomed identify and track the particular object through the trained model from the captured video.
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