Specialised pest and disease control in the agricultural crops industry have been a high-priority issue. Due to great cost-effectiveness and efficient automation, computer vision (CV)–based automatic pest or disease identification techniques are widely utilised in the smart agricultural systems. As rapid development of artificial intelligence, in the field of computer vision–based agricultural pest identification, an increasing number of scholars have begun to move their attentions from traditional machine learning models to deep learning techniques. However, so far, deep learning techniques still have been suffering from many problems such as limited data samples, cost-effectiveness of network structure, and high image quality requirements. These issues greatly limit the potential utilisation of deep-learning techniques into smart agricultural systems. This paper aims at investigating utilization of one new deep-learning model WRN (wide residual networks) into CV-based automatic disease identification problem. We first built up a large-scale agricultural disease images dataset containing over 36,000 pieces of diseases, which includes typical types of disease in tomato, potato, grape, corn and apple. Then, we analysed and evaluated wide residual networks algorithm using the Tesla K80 graphics processor (GPU) in the TensorFlow deep-learning framework. A set of comprehensive experimental protocols have been designed in comparing with GoogLeNet Inception V4 regarding several benchmarks. The experimental results indicate that (1) under WRN architecture, Softmax loss function gives a faster convergence and improved accuracy than GoogLeNet inception V4 network. (2) While WRN shows a good effect for identification of agricultural diseases, its effectiveness has a strong need on the number of training samples of dataset like at least 36 k images in our experiment. (3) The overall performance is better than 800 sheets. The disease identification results show that the WRN model can be applied to the identification of agricultural diseases.

中文翻译：

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智能农业系统中基于广泛残差网络的农业病害识别实验分析与评估

农作物行业的专门病虫害控制是一个高度优先的问题。由于具有很高的成本效益和高效的自动化，基于计算机视觉（CV）的病虫害自动识别技术已广泛应用于智能农业系统中。随着人工智能的快速发展，在基于计算机视觉的农业害虫识别领域，越来越多的学者开始将注意力从传统的机器学习模型转移到深度学习技术。然而，到目前为止，深度学习技术仍然遭受许多问题的困扰，例如数据样本有限，网络结构的成本效益以及对图像质量的高要求。这些问题极大地限制了深度学习技术在智能农业系统中的潜在利用。本文旨在研究将一种新的深度学习模型WRN（广域残差网络）用于基于CV的疾病自动识别问题。我们首先建立了一个大型农业疾病图像数据集，其中包含36,000多种疾病，其中包括番茄，马铃薯，葡萄，玉米和苹果中的典型疾病类型。然后，我们在TensorFlow深度学习框架中使用Tesla K80图形处理器（GPU）分析和评估了宽残差网络算法。在与GoogLeNet Inception V4进行比较时，已经设计了一套全面的实验方案，以比较一些基准。实验结果表明（1）在WRN架构下，与GoogLeNet Inception V4网络相比，Softmax损失功能可提供更快的收敛速度和更高的准确性。（2）虽然WRN在识别农业疾病方面显示出良好的效果，但其有效性对数据集的训练样本数量（如本实验中至少36 k张图像）有强烈的需求。（3）整体性能优于800张。病害鉴定结果表明，WRN模型可用于农业病害的鉴定。