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首页> 外文期刊>Sustainable Computing >A new Conv2D model with modified ReLU activation function for identification of disease type and severity in cucumber plant
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A new Conv2D model with modified ReLU activation function for identification of disease type and severity in cucumber plant

机译：一种新的Conv2D模型，具有改进的Relu活化功能，用于鉴定黄瓜植物疾病类型和严重程度

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摘要

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.
机译：黄瓜是大多数县的重要作物和农民的一个正在培养黄瓜作物。 通常，这种作物感染了角度点，炭疽病等。过去的研究界已经开发出各种学习模型，以识别早期黄瓜作物中的疾病，并报告的最高精度为85.7％。 在拟议的工作中，已经讨论了卷积神经网络的方法，通过实现93.75％的准确性，疾病鉴定得到了8.05％。 所提出的模型已经培训了超参数和激活功能的不同组合。 但是，通过引入修改的Relu激活功能来实现最佳精度。 还提出了分割算法来估计疾病的严重程度。 为建立拟议模型的功效，其性能与其他CNN模型以及传统的机器学习方法进行了比较。

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