Mazumder Md Khairul Alam, Kabir Md Mohsin, Rahman Ashifur, Abdullah-Al-Jubair Md, Mridha M F
Department of Computer Science, American International University-Bangladesh, Dhaka-1229, Bangladesh.
Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka-1216, Bangladesh.
Heliyon. 2024 Aug 5;10(15):e35625. doi: 10.1016/j.heliyon.2024.e35625. eCollection 2024 Aug 15.
Plant leaf diseases are a significant concern in agriculture due to their detrimental impact on crop productivity and food security. Effective disease management depends on the early and accurate detection and diagnosis of these conditions, facilitating timely intervention and mitigation strategies. In this study, we address the pressing need for accurate and efficient methods for detecting leaf diseases by introducing a new architecture called DenseNet201Plus. DenseNet201 was modified by including superior data augmentation and pre-processing techniques, an attention-based transition mechanism, multiple attention modules, and dense blocks. These modifications enhance the robustness and accuracy of the proposed DenseNet201Plus model in diagnosing diseases related to plant leaves. The proposed architecture was trained using two distinct datasets: Banana Leaf Disease and Black Gram Leaf Disease. Through extensive experimentation, we evaluated the performance of DenseNet201Plus in terms of various classification metrics and achieved values of 0.9012, 0.9012, 0.9012, and 0.9716 for accuracy, precision, recall, and AUC for the banana leaf disease dataset, respectively. Similarly, the black gram leaf disease dataset model provides values of 0.9950, 0.9950, 0.9950, and 1.0 for accuracy, precision, recall, and AUC. Compared to other well-known pre-trained convolutional neural network (CNN) architectures, our proposed model demonstrates superior performance in both utilized datasets. Last but not least, we combined the strength of Grad-CAM++ with our proposed model to enhance the interpretability and localization of disease areas, providing valuable insights for agricultural practitioners and researchers to make informed decisions and optimize disease management strategies.
植物叶片病害因其对作物产量和粮食安全的不利影响而成为农业领域的一个重大问题。有效的病害管理依赖于对这些病害的早期准确检测和诊断，以便及时采取干预和缓解策略。在本研究中，我们通过引入一种名为DenseNet201Plus的新架构，满足了对准确高效的叶片病害检测方法的迫切需求。DenseNet201通过纳入卓越的数据增强和预处理技术、基于注意力的过渡机制、多个注意力模块以及密集块进行了改进。这些改进提高了所提出的DenseNet201Plus模型在诊断与植物叶片相关病害方面的鲁棒性和准确性。所提出的架构使用两个不同的数据集进行训练：香蕉叶病害数据集和黑豆叶病害数据集。通过广泛的实验，我们根据各种分类指标评估了DenseNet201Plus的性能，香蕉叶病害数据集的准确率、精确率、召回率和AUC分别达到了0.9012、0.9012、0.9012和0.9716。同样，黑豆叶病害数据集模型的准确率、精确率、召回率和AUC分别为0.9950、0.9950、0.9950和1.0。与其他著名的预训练卷积神经网络（CNN）架构相比，我们提出的模型在两个使用的数据集中均表现出卓越的性能。最后但同样重要的是，我们将Grad-CAM++的优势与我们提出的模型相结合，以增强病害区域的可解释性和定位，为农业从业者和研究人员提供有价值的见解，以便他们做出明智的决策并优化病害管理策略。
