Department of Information Systems College of Computer and Information Sciences, Jouf University, Jouf, Saudi Arabia.
Department of Computer Science, College of Arts and Sciences at Tabarjal, Jouf University, Jouf, Saudi Arabia.
Comput Intell Neurosci. 2022 Jul 31;2022:3998193. doi: 10.1155/2022/3998193. eCollection 2022.
It has been noted that disease detection approaches based on deep learning are becoming increasingly important in artificial intelligence-based research in the field of agriculture. Studies conducted in this area are not at the level that is desirable due to the diversity of plant species and the regional characteristics of many of these species. Although numerous researchers have studied diseases on plant leaves, it is undeniable that timely diagnosis of diseases on olive leaves remains a difficult task. It is estimated that people have been cultivating olive trees for 6000 years, making it one of the most useful and profitable fruit trees in history. Symptoms that appear on infected leaves can vary from one plant to another or even between individual leaves on the same plant. Because olive groves are susceptible to a variety of pathogens, including bacterial blight, olive knot, , and olive peacock spot, it has been difficult to develop an effective olive disease detection algorithm. For this reason, we developed a unique deep ensemble learning strategy that combines the convolutional neural network model with vision transformer model. The goal of this method is to detect and classify diseases that can affect olive leaves. In addition, binary and multiclassification systems based on deep convolutional models were used to categorize olive leaf disease. The results are encouraging and show how effectively CNN and vision transformer models can be used together. Our model outperformed the other models with an accuracy of about 96% for multiclass classification and 97% for binary classification, as shown by the experimental results reported in this study.
已经注意到，基于深度学习的疾病检测方法在农业领域的人工智能研究中变得越来越重要。由于植物物种的多样性和许多这些物种的区域特征，该领域的研究还没有达到理想的水平。尽管许多研究人员已经研究了植物叶片上的疾病，但不可否认的是，及时诊断橄榄叶上的疾病仍然是一项艰巨的任务。据估计，人类已经种植橄榄树 6000 年了，使它成为历史上最有用和最有利可图的果树之一。受感染叶片上出现的症状可能因植株之间或同一植株的不同叶片之间而有所不同。由于橄榄树容易受到各种病原体的影响，包括细菌性黑斑病、橄榄结、和橄榄孔雀斑，因此很难开发出有效的橄榄疾病检测算法。出于这个原因，我们开发了一种独特的深度集成学习策略，将卷积神经网络模型与视觉变压器模型相结合。该方法的目的是检测和分类可能影响橄榄叶的疾病。此外，还使用基于深度卷积模型的二进制和多分类系统对橄榄叶疾病进行分类。实验结果令人鼓舞，表明 CNN 和视觉变压器模型可以有效地结合使用。我们的模型在多类分类中的准确率约为 96%，在二进制分类中的准确率约为 97%，优于其他模型，如本研究报告的实验结果所示。
