A new Conv2D model with modified ReLU activation function for identification of disease type and severity in cucumber plant

Cucumber is one of the important crop and farmers of most of the counties are cultivating the cucumber crop. Generally, this crop is infected with Angular Spot, Anthracnose, etc. In past research community has developed various learning models to identify the disease in cucumber crop in early-stage and reported maximum accuracy of 85.7%. In proposed work, a convolution neural network based approach has been discussed and disease identification is improved by 8.05% by achieving the accuracy of 93.75%. The proposed model has been trained on a different combination of hyperparameters and activation function. However, the best accuracy has been achieved by introducing a modified ReLU activation function. A segmentation algorithm has also been proposed to estimate the severity of the disease. To establish the efficacy of the proposed model, its performance has been compared with other CNN models as well as traditional machine learning methods.

Automated summary

The study improved the accuracy of cucumber crop disease identification and proposed an approach based on convolutional neural networks. By introducing a modified ReLU activation function, the method achieved an accuracy of 93.75%.