Cao Yifei, Yuan Peisen, Xu Huanliang, Martínez-Ortega José Fernán, Feng Jiarui, Zhai Zhaoyu
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China.
College of Engineering, Nanjing Agricultural University, Nanjing, China.
Front Plant Sci. 2022 Jul 13;13:963170. doi: 10.3389/fpls.2022.963170. eCollection 2022.
Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky-Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention.
水稻是人类最重要的粮食作物之一。其总产量在谷物作物产量中排名第三。水稻白叶枯病作为水稻三大主要病害之一，每年都会发生，对水稻生产和安全构成巨大威胁。在感染期和发病期之间存在无症状期，并且在适宜条件下白叶枯病会迅速广泛传播。因此，准确检测早期无症状白叶枯病非常必要。本研究的目的是基于高光谱成像和光谱扩张卷积三维卷积神经网络（SDC - 3DCNN）测试检测水稻白叶枯病早期无症状感染的可行性。首先，从分蘖期感染白叶枯病的水稻叶片获取高光谱图像。通过Savitzky - Golay（SG）方法对光谱进行平滑处理，并截取450至950nm之间的波长进行分析。然后使用主成分分析（PCA）和随机森林（RF）从原始光谱中提取特征信息作为输入。评估了具有不同数量输入特征和不同光谱扩张率的SDC - 3DCNN模型的整体性能。最后，使用显著性图可视化来解释各个波长的敏感性。结果表明，当输入数量为50个特征波长（由RF提取）且扩张率设置为5时，SDC - 3DCNN模型的性能达到了95.4427%的准确率。在530至570nm范围内识别出了显著性敏感波长，这与RF提取的重要波长重叠。根据我们的研究结果，结合高光谱成像和深度学习可以成为识别水稻白叶枯病早期无症状感染的可靠方法，为早期预警和水稻病害预防提供充分支持。
