| Plants pests and diseases can cause significant economic losses for farmers, and accurate identification and classification of these issues is crucial. Deep learning techniques, have demonstrated promising results in correctly identifying and classifying plant pests and diseases. However, the accuracy of these models heavily relies on the quality and size of the training dataset. Gathering a large and diverse dataset of plant pests and diseases can be challenging due to the variation in the appearance of diseased plants and the high cost of data collection. To tackle this issue, a web portal has been developed to gather datasets of plant pests and diseases, making it simpler for researchers to contribute data and enhance the accuracy of these models. In addition, Generative Adversarial Networks (GANs) have been used to expand the current datasets by creating synthetic images that closely resemble real-world data. This has resulted in improved accuracy and robustness of CNN and YOLO models in identifying and classifying plant |
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