■作物害虫对作物的品质和产量有很大影响。使用深度学习识别作物害虫对于作物精确管理非常重要。
■为了解决当前害虫研究中缺乏数据集和分类准确性差的问题,建立了大规模害虫数据集HQIP102,提出了害虫识别模型MADN。IP102大型农作物害虫数据集存在一些问题,例如某些害虫类别是错误的,图像中缺少害虫主题。在这项研究中,对IP102数据集进行仔细过滤,得到HQIP102数据集,其中包含八种作物的102种害虫类别的47,393张图像。MADN模型从三个方面提高了DenseNet的表示能力。首先,选择性内核单元被引入到DenseNet模型中,可以根据输入自适应调整感受野的大小,更有效地捕获不同大小的目标物体。其次,为了使特征服从稳定分布,DenseNet模型中使用了代表性批标准化模块。此外,自适应选择是否激活神经元可以提高网络的性能,在DenseNet模型中使用了ACON激活函数。最后,MADN模型由集成学习构成。
■实验结果表明,MADN在HQIP102数据集上实现了75.28%和65.46%的准确性和F1得分,与改善前的DenseNet-121相比,改善了5.17个百分点和5.20个百分点。与ResNet-101相比,MADN模型的准确性和F1Score分别提高了10.48个百分点和10.56个百分点,而参数大小下降了35.37%。使用移动应用程序将模型部署到云服务器有助于确保作物产量和质量。
UNASSIGNED: Crop pests have a great impact on the quality and yield of crops. The use of deep learning for the identification of crop pests is important for crop precise management.
UNASSIGNED: To address the lack of data set and poor classification accuracy in current pest research, a large-scale pest data set named HQIP102 is built and the pest identification model named MADN is proposed. There are some problems with the IP102 large crop pest dataset, such as some pest categories are wrong and pest subjects are missing from the images. In this study, the IP102 data set was carefully filtered to obtain the HQIP102 data set, which contains 47,393 images of 102 pest classes on eight crops. The MADN model improves the representation capability of DenseNet in three aspects. Firstly, the Selective Kernel unit is introduced into the DenseNet model, which can adaptively adjust the size of the receptive field according to the input and capture target objects of different sizes more effectively. Secondly, in order to make the features obey a stable distribution, the Representative Batch Normalization module is used in the DenseNet model. In addition, adaptive selection of whether to activate neurons can improve the performance of the network, for which the ACON activation function is used in the DenseNet model. Finally, the MADN model is constituted by ensemble learning.
UNASSIGNED: Experimental results show that MADN achieved an accuracy and F1Score of 75.28% and 65.46% on the HQIP102 data set, an improvement of 5.17 percentage points and 5.20 percentage points compared to the pre-improvement DenseNet-121. Compared with ResNet-101, the accuracy and F1Score of MADN model improved by 10.48 percentage points and 10.56 percentage points, while the parameters size decreased by 35.37%. Deploying models to cloud servers with mobile application provides help in securing crop yield and quality.