深度学习方法在花生病害检测中的有效性

Ramazan Kursun, Elham Tahsin Yasin, Murat Koklu
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引用次数: 0

摘要

早期发现农业部门的植物病害被认为是提高生产力和减少损害的一个重要目标。本研究涉及利用深度学习方法实现花生叶片病害的自动检测,以及病害检测过程中形成的热图可视化模型的可解释性。在这项研究中,使用了一个包含3058张图像的数据集,其中包含5类富含患病和健康花生叶样本的图像。还研究了可解释性属性,以了解为什么模型检测到特定疾病。本文采用热图方法将通常被描述为“魔盒”的深度学习模型的决策过程可视化。通过热图可视化突出显示有效检测病叶的像素点,试图使模型的决策过程易于理解。结果表明,深度学习模型在花生叶病检测中具有较高的性能,热图可视化获得的可解释性为农业专家和生产者提供了可靠的工具。由于模型提供的可视化解释,提高了对疾病检测的信心水平,并为模型的决策过程提供了信心。本研究通过研究花生植物疾病检测领域的深度学习方法的影响和可解释性,为提高农业应用效率和提供更有效的疾病管理方法迈出了重要的一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Effectiveness of Deep Learning Methods on Groundnut Disease Detection
Early detection of plant diseases in the agricultural sector is considered an important goal to increase productivity and minimize damage. This study deals with the use of deep learning methods to realize the automatic detection of leaf diseases in peanut plants and the explicability of the model with heatmap visualizations formed during the detection of diseases. In the study, a dataset containing 3058 images with 5 classes enriched with diseased and healthy samples of peanut leaves was used. The explainability property has also been studied to understand why the models detect a particular disease. The decision processes of deep learning models, which are usually described as the "magic box", were visualized with the heatmap method in this study. By highlighting the pixels that are effective in detecting diseased leaves with heatmap visualization, the decision-making process of the model has been tried to be made understandable. The results show that deep learning models have high performance in detecting peanut leaf diseases, and the explainability obtained by heatmap visualization is a reliable tool for agricultural specialists and producers. Thanks to the visual explanations provided by the model, the level of confidence in the detection of diseases has been increased and confidence in the decision processes of the model has been provided. This study constitutes an important step towards increasing efficiency in agricultural applications and providing a more efficient approach to disease management by investigating the impact and explicability of deep learning methods in the field of disease detection in peanut plants.
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