论文标题
基于深网的玉米疾病识别的有效计划
An Effective Scheme for Maize Disease Recognition based on Deep Networks
论文作者
论文摘要
在过去的几十年中，由于其在人类，牲畜和家禽的食品周期中起着至关重要的作用，因此玉米产品种植的地区增加了。此外，植物的疾病会影响食品安全，并可以大大降低农产品的质量和数量。准确及时诊断该疾病面临许多挑战。这项研究提出了一种基于深层神经网络的新计划，以克服上述挑战。由于数据数量有限，因此在两个众所周知的架构的帮助下采用了转移学习技术。这样，由于对象检测问题的有效性能，预先训练的MobilenetV2和Inception网络的组合将采用一种新的有效模型。 Moblienetv2和Inception模块的卷积层是与早期层的排列，以提取关键特征。此外，通过增强策略解决了班级的不平衡问题。与近年来发表的其他最新模型相比，该计划的方案的性能优于性能。该模型的准确性大约达到97％。总而言之，实验结果证明了该方法在诊断植物叶中诊断疾病方面的有效性和显着性能。
In the last decades, the area under cultivation of maize products has increased because of its essential role in the food cycle for humans, livestock, and poultry. Moreover, the diseases of plants impact food safety and can significantly reduce both the quality and quantity of agricultural products. There are many challenges to accurate and timely diagnosis of the disease. This research presents a novel scheme based on a deep neural network to overcome the mentioned challenges. Due to the limited number of data, the transfer learning technique is employed with the help of two well-known architectures. In this way, a new effective model is adopted by a combination of pre-trained MobileNetV2 and Inception Networks due to their effective performance on object detection problems. The convolution layers of MoblieNetV2 and Inception modules are parallelly arranged as earlier layers to extract crucial features. In addition, the imbalance problem of classes has been solved by an augmentation strategy. The proposed scheme has a superior performance compared to other state-of-the-art models published in recent years. The accuracy of the model reaches 97%, approximately. In summary, experimental results prove the method's validity and significant performance in diagnosing disease in plant leaves.
