轻量化卷积神经网络在苹果叶片病害识别中的应用

IF 4.1 2区 生物学 Q1 PLANT SCIENCES
Lili Fu, Shijun Li, Yu Sun, Ye Mu, Tianli Hu, He Gong
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引用次数: 6

摘要

作为一种世界范围内广泛食用的水果,果树病害的防治显得尤为重要。在本研究中,我们基于AlexNet模型设计了卷积神经网络(cnn),针对影响苹果树叶的五种疾病。首先,在模型中使用扩张卷积提取疾病的粗粒度特征,这有助于在减少参数数量的同时保持较大的接受野。加入平行卷积模块,提取多尺度叶片病害特征。随后,系列3 × 3卷积的快捷连接允许模型处理额外的非线性。此外,将注意力机制添加到所有聚合输出模块中,以更好地拟合通道特征,并减少复杂背景对模型性能的影响。最后,用全局池化取代两个完全连接的层,减少模型参数的数量,保证特征不丢失。模型的最终识别准确率为97.36%,模型大小为5.87 MB。与其他5个模型相比,我们的模型设计合理,具有较好的鲁棒性;此外,研究结果表明,该模型具有轻量化特点,能够以较高的准确率识别苹果叶片病害。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lightweight-Convolutional Neural Network for Apple Leaf Disease Identification
As a widely consumed fruit worldwide, it is extremely important to prevent and control disease in apple trees. In this research, we designed convolutional neural networks (CNNs) for five diseases that affect apple tree leaves based on the AlexNet model. First, the coarse-grained features of the disease are extracted in the model using dilated convolution, which helps to maintain a large receptive field while reducing the number of parameters. The parallel convolution module is added to extract leaf disease features at multiple scales. Subsequently, the series 3 × 3 convolutions shortcut connection allows the model to deal with additional nonlinearities. Further, the attention mechanism is added to all aggregated output modules to better fit channel features and reduce the impact of a complex background on the model performance. Finally, the two fully connected layers are replaced by global pooling to reduce the number of model parameters, to ensure that the features are not lost. The final recognition accuracy of the model is 97.36%, and the size of the model is 5.87 MB. In comparison with five other models, our model design is reasonable and has good robustness; further, the results show that the proposed model is lightweight and can identify apple leaf diseases with high accuracy.
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来源期刊
Frontiers in Plant Science
Frontiers in Plant Science PLANT SCIENCES-
CiteScore
7.30
自引率
14.30%
发文量
4844
审稿时长
14 weeks
期刊介绍: In an ever changing world, plant science is of the utmost importance for securing the future well-being of humankind. Plants provide oxygen, food, feed, fibers, and building materials. In addition, they are a diverse source of industrial and pharmaceutical chemicals. Plants are centrally important to the health of ecosystems, and their understanding is critical for learning how to manage and maintain a sustainable biosphere. Plant science is extremely interdisciplinary, reaching from agricultural science to paleobotany, and molecular physiology to ecology. It uses the latest developments in computer science, optics, molecular biology and genomics to address challenges in model systems, agricultural crops, and ecosystems. Plant science research inquires into the form, function, development, diversity, reproduction, evolution and uses of both higher and lower plants and their interactions with other organisms throughout the biosphere. Frontiers in Plant Science welcomes outstanding contributions in any field of plant science from basic to applied research, from organismal to molecular studies, from single plant analysis to studies of populations and whole ecosystems, and from molecular to biophysical to computational approaches. Frontiers in Plant Science publishes articles on the most outstanding discoveries across a wide research spectrum of Plant Science. The mission of Frontiers in Plant Science is to bring all relevant Plant Science areas together on a single platform.
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