Jain Achin, Dubey Arun Kumar, Pan Vincent Shin-Hung, Mahfoudh Saoucene, Althaqafi Turki A, Arya Varsha, Alhalabi Wadee, Singh Sunil K, Jain Vanita, Panwar Arvind, Kumar Sudhakar, Hsu Ching-Hsien, Gupta Brij B
Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India.
Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan.
Sci Rep. 2025 Aug 25;15(1):31259. doi: 10.1038/s41598-025-15940-7.
Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based approach that optimizes CNN models through Bayesian optimization and ensemble learning. In the proposed study, extensive experiments were conducted to develop an optimized Bayesian Weighted Ensemble CNN model for the detection of potato leaf blight. First, multiple CNN architectures were trained using different optimizers: ADAM (DL1), SGD (DL2), RMSProp (DL3), and ADAMAX (DL4), evaluating their individual performance. To mitigate class imbalance, data augmentation techniques were applied, increasing the number of healthy leaves by 6 times. In addition, fuzzy image enhancement was implemented to improve feature extraction and classification accuracy. Bayesian optimization was then used to determine the optimal weights for a deep ensemble model, exploring 11 possible model combinations. The final EDL7 ensemble model (DL1 + DL2 + DL3), optimized through Bayesian optimization, achieved the highest accuracy of 97.94%, outperforming individual models. Furthermore, the ensemble model achieved a precision of 0.981, recall of 0.983, and an F1 score of 0.982, ensuring a well-balanced trade-off between precision and recall. These results highlight the effectiveness of Bayesian-optimized ensemble learning in improving potato blight detection, making it a robust and reliable solution for agricultural disease classification.
马铃薯晚疫病是一种严重影响马铃薯作物的病害，会导致巨大的农业和经济损失。为提高检测准确率，我们提出了贝叶斯优化的卷积神经网络加权集成马铃薯晚疫病检测方法，这是一种基于深度学习的方法，通过贝叶斯优化和集成学习对卷积神经网络模型进行优化。在所提出的研究中，我们进行了广泛的实验，以开发一种用于检测马铃薯叶疫病的优化贝叶斯加权集成卷积神经网络模型。首先，使用不同的优化器训练多个卷积神经网络架构：ADAM（DL1）、随机梯度下降（SGD，DL2）、RMSProp（DL3）和Adamax（DL4），评估它们各自的性能。为缓解类别不平衡问题，应用了数据增强技术，将健康叶片的数量增加了6倍。此外，实施了模糊图像增强以提高特征提取和分类准确率。然后使用贝叶斯优化来确定深度集成模型的最优权重，探索11种可能的模型组合。通过贝叶斯优化优化后的最终EDL7集成模型（DL1 + DL2 + DL3）实现了97.94%的最高准确率，优于单个模型。此外，该集成模型的精确率为0.981，召回率为0.983，F1分数为0.982，确保了精确率和召回率之间的良好平衡。这些结果突出了贝叶斯优化的集成学习在改善马铃薯晚疫病检测方面的有效性，使其成为农业病害分类的一种强大且可靠的解决方案。
