论文标题
一种深度学习方法，用于确定tuta absoluta在番茄植物中的影响
A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants
论文作者
论文摘要
早期对Tuta Absoluta Pest在番茄植物中的影响的早期量化是控制和防止严重损害害虫的非常重要的因素。 Tuta Absoluta的入侵被认为是对番茄产量的主要威胁，导致造成重大损失在80％至100％的情况下，如果无法正确管理。因此，对番茄叶矿工图塔绝对的实时和早期量化可以在解决害虫管理问题并增强农民的决定方面发挥重要作用。在这项研究中，我们提出了一种卷积神经网络（CNN）方法来确定tuta absoluta对番茄植物的影响。在培训分类器中使用了四个CNN预训练的架构（VGG16，VGG19，Resnet和Inception-V3），这些分类器在包含健康实验中收集的健康和感染的番茄叶的数据集中。在预先训练的架构中，实验结果表明，Inpection-V3在估计番茄植物中tuta absoluta的严重程度状态时的平均准确度为87.2％。与其他严重程度状态相比，预训练的模型也可以轻松识别高tuta严重性状态（低tuta和no tuta）
Early quantification of Tuta absoluta pest's effects in tomato plants is a very important factor in controlling and preventing serious damages of the pest. The invasion of Tuta absoluta is considered a major threat to tomato production causing heavy loss ranging from 80 to 100 percent when not properly managed. Therefore, real-time and early quantification of tomato leaf miner Tuta absoluta, can play an important role in addressing the issue of pest management and enhance farmers' decisions. In this study, we propose a Convolutional Neural Network (CNN) approach in determining the effects of Tuta absoluta in tomato plants. Four CNN pre-trained architectures (VGG16, VGG19, ResNet and Inception-V3) were used in training classifiers on a dataset containing health and infested tomato leaves collected from real field experiments. Among the pre-trained architectures, experimental results showed that Inception-V3 yielded the best results with an average accuracy of 87.2 percent in estimating the severity status of Tuta absoluta in tomato plants. The pre-trained models could also easily identify High Tuta severity status compared to other severity status (Low tuta and No tuta)
