ABSTRACT

Pest recognition in the agriculture field plays a critical issue for the farmers which diminishes economic growth. So far, traditional practices were followed by farmers to increase yield production. Nowadays, researchers execute a deep learning approach to classify various kinds of images practically. In this paper, Deep convolutional neural networks (DCNN) are used to recognise ten kinds of pests present in the paddy crop. The data repository contains around 3549 pest images that affect the paddy crops, Since Deep Learning supports well for larger data-set so the data augmentation process is carried out. The neural model is build using various kinds of DCNN architecture, interpretation was made over the models based on the accuracy rate and the performance. The transfer learning approach is applied over the pest data set by fine-tuning the hyperparameters and the layers of the ResNet-50 model. By comparing the resultant value, the fine-tuned ResNet-50 model produced better accuracy of 95.012% among other models. The obtained resultant value describes the effective performance of the model in pest disease classification.

摘要

农业领域的病虫害识别对农民来说是一个关键问题，这会减少经济增长。到目前为止，农民遵循传统做法来提高产量。如今，研究人员使用深度学习方法对各种图像进行实际分类。在本文中，深度卷积神经网络（DCNN）用于识别水稻作物中存在的十种害虫。数据存储库包含大约 3549 个影响水稻作物的害虫图像，由于深度学习很好地支持更大的数据集，因此执行了数据增强过程。神经模型是使用各种 DCNN 架构构建的，根据准确率和性能对模型进行解释。通过微调 ResNet-50 模型的超参数和层，将迁移学习方法应用于害虫数据集。通过比较结果值，经过微调的 ResNet-50 模型在其他模型中产生了 95.012% 的更高准确率。获得的结果值描述了模型在病虫害分类中的有效性能。