Cai Jitong, Pan Renyong, Lin Jianwu, Liu Jiaming, Zhang Licai, Wen Xingtian, Chen Xiaoyulong, Zhang Xin
College of Big Data and Information Engineering, Guizhou University, Guiyang, China.
Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University, Guizhou Provincial Science and Technology Department, Guiyang, China.
Front Plant Sci. 2023 Sep 11;14:1224385. doi: 10.3389/fpls.2023.1224385. eCollection 2023.
Corn is one of the world's essential crops, and the presence of corn diseases significantly affects both the yield and quality of corn. Accurate identification of corn diseases in real time is crucial to increasing crop yield and improving farmers' income. However, in real-world environments, the complexity of the background, irregularity of the disease region, large intraclass variation, and small interclass variation make it difficult for most convolutional neural network models to achieve disease recognition under such conditions. Additionally, the low accuracy of existing lightweight models forces farmers to compromise between accuracy and real-time.
To address these challenges, we propose FCA-EfficientNet. Building upon EfficientNet, the fully-convolution-based coordinate attention module allows the network to acquire spatial information through convolutional structures. This enhances the network's ability to focus on disease regions while mitigating interference from complex backgrounds. Furthermore, the adaptive fusion module is employed to fuse image information from different scales, reducing interference from the background in disease recognition. Finally, through multiple experiments, we have determined the network structure that achieves optimal performance.
Compared to other widely used deep learning models, this proposed model exhibits outstanding performance in terms of accuracy, precision, recall, and F1 score. Furthermore, the model has a parameter count of 3.44M and Flops of 339.74M, which is lower than most lightweight network models. We designed and implemented a corn disease recognition application and deployed the model on an Android device with an average recognition speed of 92.88ms, which meets the user's needs.
Overall, our model can accurately identify corn diseases in realistic environments, contributing to timely and effective disease prevention and control.
玉米是世界主要作物之一，玉米病害的存在显著影响玉米的产量和质量。实时准确识别玉米病害对于提高作物产量和增加农民收入至关重要。然而，在实际环境中，背景的复杂性、病害区域的不规则性、类内差异大以及类间差异小使得大多数卷积神经网络模型难以在这种条件下实现病害识别。此外，现有轻量级模型的低准确率迫使农民在准确率和实时性之间做出妥协。
为应对这些挑战，我们提出了FCA-EfficientNet。基于EfficientNet构建，基于全卷积的坐标注意力模块使网络能够通过卷积结构获取空间信息。这增强了网络专注于病害区域的能力，同时减轻了复杂背景的干扰。此外，采用自适应融合模块融合来自不同尺度的图像信息，减少病害识别中背景的干扰。最后，通过多次实验，我们确定了实现最佳性能的网络结构。
与其他广泛使用的深度学习模型相比，该模型在准确率、精确率、召回率和F1分数方面表现出色。此外，该模型的参数数量为344万，浮点运算次数为3.3974亿，低于大多数轻量级网络模型。我们设计并实现了一个玉米病害识别应用程序，并将该模型部署在安卓设备上，平均识别速度为92.88毫秒，满足了用户需求。
总体而言，我们的模型能够在现实环境中准确识别玉米病害，有助于及时有效地进行病害防控。
