Crop Leaf Disease Detection Using DCCN

The automatic identification of crop leaf disease detection (CLDD) is very useful for the agricultural field. At present deep learning is a very hot research topic to discuss, it can successfully solve the problems of farmers. This paper presents the Deep Convolutional Neural Network (DCNN) for improving the accuracy of crop leaf disease detection. Further, the consequence of the offered algorithm is analyzed using data augmentation to minimize the data scarcity problem that arises due to uneven dataset size. The consequence of the offered strategy is analyzed on the PlantVillage dataset for the tomato plant based on accuracy, recall, precision, and F1-score. It is observed that the offered strategy provides noteworthy improvement over the traditional state of arts.