全球消费的大部分食物都是由植物生产的。植物病害是减产的主要原因,但可以通过定期监测来管理。人工观察植物病害需要更多的时间,而且容易出错。借助人工智能和计算机视觉早期发现植物病害可以减少病害的影响,并帮助植物抵御持续监测的不利影响。在这份手稿中,提出了使用动态差分退火优化算法(PDI-CMAE-DDAOA)优化的上下文掩码自动编码器进行植物病害识别。植物村数据集用于收集图像。然后将图像馈送到预处理。使用自适应自引导滤波器方法,在预处理阶段从输入图像中去除噪声。预处理部分的结果用作特征提取段的输入。四个统计特征,包括平均,方差,熵,和峰度,从余弦相似性隐马尔可夫模型(CSHMM)中恢复。上下文掩模自动编码器(CMAE)被赋予提取的特征,以准确地分类植物图像的健康和不健康区域。慢收敛的问题影响CMAE。然而,注意,在这种情况下,与纹理特征相比,CMAE与深度学习特征的收敛更快。CMAE分类器通常不表现出用于确定最佳参数以确保植物病害的精确分类的优化算法的任何适应性。因此,动态差分退火优化算法(DDAOA)被认为是增强CMAE分类器,准确区分健康和患病植物。所提出的PDI-CMAE-DDAOA是在Python中完成的。PDI-CMAE-DDAOA的疗效在一些性能指标下进行评估,比如准确性,精度,灵敏度,F1分数,特异性,错误率,接收机工作特性曲线(ROC),计算时间。所提出的方法提供了更高的精度23.34%,34.33%,和32.07%;灵敏度更高36.67%,36.33%,和23.21%;较高的F1得分46.67%,57.56%,和43.21%;更高的特异性56.67%,67.56%,23.21%的人使用现有模型进行分析,如基于迁移学习的深度集成神经网络,用于植物叶片感染识别(PDI-DENN),基于卷积自动编码器和卷积神经网络混合模型的植物病害检测(PDI-CAE-CNN),以及取决于深度学习方法(PDI-EN-CNN)的自动可靠的叶片病害发现,分别。研究重点:在早期发现植物病害。介绍PDI-CMAE-DDOA。借助高效的CSHMM提取最优特征以获得更好的分类精度。最小化分类过程中的误差。最大化曲线下的高面积值。
Most of the food consumed worldwide is produced by plants. Plant disease is a major cause of reduced production, but can be managed with regular monitoring. Manually observing plant diseases takes more time and is error-prone. Early detection of plant diseases with the aid of artificial intelligence and computer vision can decrease the effects of disease and help plants withstand the downsides of continuing surveillance. In this manuscript, plant disease identification using contextual mask auto-encoder optimized with dynamic differential annealed optimization algorithm (PDI-CMAE-DDAOA) is proposed. The plant village dataset is used to collect the images. Then the image is fed to preprocessing. Using an adaptive self-guided filter approach, the noise is removed from the input images during the pre-processing phase. The result of the pre-processing section serves as input for the feature extraction segment. Four statistical features, including mean, variance, entropy, and kurtosis, are recovered from the cosine similarity hidden Markov model (CSHMM). The contextual mask auto-encoder (CMAE) is given the extracted features to accurately classify the healthy and unhealthy regions of the plant image. The issue of slow convergence affects the CMAE. However, it is noted that the CMAE converges more quickly with deep learning features than with texture features in this instance. The CMAE classifier generally does not exhibit any adaptation of optimization algorithms for determining the best parameters to ensure the precise classification of plant disease. Therefore, dynamic differential annealed optimization algorithm (DDAOA) is considered to enhance the CMAE classifier, which accurately distinguishes between healthy and diseased plants. The proposed PDI-CMAE-DDAOA is done in Python. The efficacy of PDI-CMAE-DDAOA is evaluated under some performance metrics, like accuracy, precision, sensitivity, F1-score, specificity, error rate, receiver operating characteristic curve (ROC), computational time. The proposed method provides higher accuracy 23.34%, 34.33%, and 32.07%; higher sensitivity 36.67%, 36.33%, and 23.21%; higher F1-score 46.67%, 57.56%, and 43.21%; higher specificity 56.67%, 67.56%, and 23.21% analyzed with existing models, like transfer learning-based deep ensemble neural network for plant leaf infection recognition (PDI-DENN), plant disease detection with hybrid model based on convolutional auto-encoder and convolutional neural network (PDI-CAE-CNN), and automatic and reliable leaf disease finding depending on deep learning methods (PDI-EN-CNN), respectively. RESEARCH HIGHLIGHTS: To find the plant disease at early stage. To present PDI-CMAE-DDAOA. To get better classification accuracy by extracting the optimal features with the help of efficient CSHMM. To minimize the error during classification process. To maximize high area under curve value.