Yang Jin, Zhu Wenke, Liu Guanqi, Dai Weisi, Xu Zhuonong, Wan Li, Zhou Guoxiong
College of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha 410004, China.
College of Bangor, Central South University of Forestry and Technology, Changsha 410004, China.
Plants (Basel). 2024 Aug 15;13(16):2277. doi: 10.3390/plants13162277.
Maize is an important crop, and the detection of maize diseases is critical for ensuring food security and improving agricultural production efficiency. To address the challenges of difficult feature extraction due to the high similarity among maize leaf disease species, the blurring of image edge features, and the susceptibility of maize leaf images to noise during acquisition and transmission, we propose a maize disease detection method based on ICPNet (Integrated multidimensional attention coordinate depthwise convolution PSO (Particle Swarm Optimization)-Integrated lion optimisation algorithm network). Firstly, we introduce a novel attention mechanism called Integrated Multidimensional Attention (IMA), which enhances the stability and responsiveness of the model in detecting small speckled disease features by combining cross-attention and spatial channel reconstruction methods. Secondly, we propose Coordinate Depthwise Convolution (CDC) to enhance the accuracy of feature maps through multi-scale convolutional processing, allowing for better differentiation of the fuzzy edges of maize leaf disease regions. To further optimize model performance, we introduce the PSO-Integrated Lion Optimisation Algorithm (PLOA), which leverages the exploratory stochasticity and annealing mechanism of the particle swarm algorithm to enhance the model's ability to handle mutation points while maintaining training stability and robustness. The experimental results demonstrate that ICPNet achieved an average accuracy of 88.4% and a precision of 87.3% on the self-constructed dataset. This method effectively extracts the tiny and fuzzy edge features of maize leaf diseases, providing a valuable reference for disease control in large-scale maize production.
玉米是一种重要的作物，玉米病害的检测对于确保粮食安全和提高农业生产效率至关重要。为应对玉米叶部病害种类相似度高导致特征提取困难、图像边缘特征模糊以及玉米叶部图像在采集和传输过程中易受噪声影响等挑战，我们提出了一种基于ICPNet（集成多维注意力坐标深度卷积粒子群优化 - 集成狮子优化算法网络）的玉米病害检测方法。首先，我们引入了一种名为集成多维注意力（IMA）的新型注意力机制，通过结合交叉注意力和空间通道重建方法，增强了模型检测小斑点病害特征的稳定性和响应能力。其次，我们提出了坐标深度卷积（CDC），通过多尺度卷积处理提高特征图的准确性，以便更好地区分玉米叶部病害区域的模糊边缘。为进一步优化模型性能，我们引入了粒子群优化 - 集成狮子优化算法（PLOA），该算法利用粒子群算法的探索随机性和退火机制，在保持训练稳定性和鲁棒性的同时增强模型处理突变点的能力。实验结果表明，ICPNet在自建数据集上的平均准确率达到88.4%，精确率达到87.3%。该方法有效地提取了玉米叶部病害微小且模糊的边缘特征，为大规模玉米生产中的病害防治提供了有价值的参考。
