Zeng Nanxin, Gong Gufeng, Zhou Guoxiong, Hu Can
College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410018, China.
Hunan Polytechnic of Environment and Biology, Hengyang 421005, China.
Plants (Basel). 2023 Jun 5;12(11):2225. doi: 10.3390/plants12112225.
Rice is a crucial food crop, but it is frequently affected by diseases during its growth process. Some of the most common diseases include rice blast, flax leaf spot, and bacterial blight. These diseases are widespread, highly infectious, and cause significant damage, posing a major challenge to agricultural development. The main problems in rice disease classification are as follows: (1) The images of rice diseases that were collected contain noise and blurred edges, which can hinder the network's ability to accurately extract features of the diseases. (2) The classification of disease images is a challenging task due to the high intra-class diversity and inter-class similarity of rice leaf diseases. This paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images. Additionally, a new neural network (ICAI-V4) is designed based on the Inception-V4 backbone structure, with a coordinate attention mechanism added to enhance feature capture and overall model performance. The INCV backbone structure incorporates Inception-iv and Reduction-iv structures, with the addition of involution to enhance the network's feature extraction capabilities from a channel perspective. This enables the network to better classify similar images of rice diseases. To address the issue of neuron death caused by the ReLU activation function and improve model robustness, Leaky ReLU is utilized. Our experiments, conducted using the 10-fold cross-validation method and 10,241 images, show that ICAI-V4 has an average classification accuracy of 95.57%. These results indicate the method's strong performance and feasibility for rice disease classification in real-life scenarios.
水稻是一种重要的粮食作物，但在其生长过程中经常受到病害影响。一些最常见的病害包括稻瘟病、胡麻叶斑病和白叶枯病。这些病害分布广泛、传染性强且造成重大损害，对农业发展构成重大挑战。水稻病害分类中的主要问题如下：（1）收集到的水稻病害图像包含噪声且边缘模糊，这会阻碍网络准确提取病害特征的能力。（2）由于水稻叶片病害的类内多样性高和类间相似度高，病害图像的分类是一项具有挑战性的任务。本文提出了Candy算法，这是一种图像增强技术，利用改进的Canny算子滤波（引力边缘检测算法）来突出水稻图像的边缘特征并最小化图像中存在的噪声。此外，基于Inception-V4骨干结构设计了一种新的神经网络（ICAI-V4），添加了坐标注意力机制以增强特征捕捉和整体模型性能。INCV骨干结构包含Inception-iv和Reduction-iv结构，并添加了卷积以从通道角度增强网络的特征提取能力。这使网络能够更好地对水稻病害的相似图像进行分类。为了解决由ReLU激活函数导致的神经元死亡问题并提高模型鲁棒性，采用了Leaky ReLU。我们使用10折交叉验证方法和10241张图像进行的实验表明，ICAI-V4的平均分类准确率为95.57%。这些结果表明该方法在实际场景中对水稻病害分类具有强大的性能和可行性。
