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.

黄瓜是重要的农作物之一，大多数县的农民都在种植黄瓜。通常，这种作物感染了角斑病，炭疽病等。过去，研究界已经开发出各种学习模型来早期识别黄瓜作物中的病害，据报道最大准确度为85.7％。在提出的工作中，已经讨论了基于卷积神经网络的方法，通过达到93.75％的准确性，疾病识别率提高了8.05％。所提出的模型已经在超参数和激活函数的不同组合上进行了训练。但是，通过引入改进的ReLU激活功能已达到最佳精度。还提出了一种分割算法来估计疾病的严重程度。为了确定所提出模型的功效，