- “Image Super-resolution processing is considered as two types of identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and
26 diseases (or the absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach.
- Overall, the approach of training deep learning models on increasingly large and publicly available image data sets presents a clear path toward smartphoneassiste crop disease diagnosis on a massive global scale
- This script runs both the machine learning model easily in one file and take an Input as a Image and gives the output as a class name. The Script uses both the functions and first enhaces the Image and then use the Plant Disease Detection model to detect the Disease in the given Input Image.