Abstract:
Potatoes are the most often consumed vegetable in many countries throughout the year, and Bangladesh is one of them. Plant diseases and venomous insects pose a significant agricultural hazard and now substantially impact Bangladesh's economy. This paper proposes a real-time technique for detecting potato leaf disease based on a deep convolutional neural network. The categorization of a picture into several categories is known as segmentation. We have used the K-means clustering algorithm for segmentation. In addition, to increase the model's efficacy, numerous data augmentation procedures have been applied to the training data. A convolutional neural network is a deep learning neural network used to prepare ordered clusters of data, such as depictions. We have used a novel CNN approach, VGG16, and ResNet50. By using VGG16, novel CNN, and resNet50, the suggested technique was able to classify potato leaves into three groups with 96, 93, and 67% accuracy, respectively. The recommended method outperforms current methodologies as we compared the performances of the models according to relevant parameters.
