Dinesh P, Lakshmanan Ramanathan
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
Sci Rep. 2025 Jul 1;15(1):21208. doi: 10.1038/s41598-025-04758-y.
Agriculture provides the basics for producing food, driving economic growth, and maintaining environmental sustainability. On the other hand, plant diseases have the potential to reduce crop productivity and raise expenses, posing a risk to food security and the incomes of farmers. Citrus plants, recognized for their nutritional benefits and economic significance, are especially vulnerable to diseases such as citrus greening, Black spot, and Citrus canker. Due to technological advancements, image processing and Deep learning algorithms can now detect and classify plant diseases early on, which assists in preserving crop health and productivity. The proposed work enables farmers to identify and visualize multiple diseases affecting citrus plants. This study proposes an efficient model to detect multiple citrus diseases (canker, black spot, and greening) that may co-occur on the same leaf. It is achieved using the RSL (Residual Squeeze & Excitation LeakyRelu) Linked-TransNet multiclass segmentation model. The proposed model stands out in its ability to address major limitations in existing models, including spatial inconsistency, loss of fine disease boundaries, and inadequate feature representation. The significance of this proposed RSL Linked-Transnet model lies in its integration of hierarchical feature extraction, global context modeling via transformers, and precise feature reconstruction, ensuring superior segmentation accuracy and robustness. The results of the proposed RSL Linked-TransNet architecture reveal average values of 0.9755 for accuracy, 0.0660 for loss, 0.9779 for precision, 0.9738 for recall, and 0.9308 for IoU. Additionally, the model achieves a mean F1 score of 0.7173 and a mean IoU of 0.7567 for each disease class in images from the test dataset. The segmentation results are further utilized to identify the prime disease affecting the leaves and evaluate disease severity using the prime disease classification and severity detection algorithm.
农业为粮食生产、推动经济增长和维持环境可持续性提供基础。另一方面，植物病害有可能降低作物产量并增加成本，对粮食安全和农民收入构成风险。柑橘类植物因其营养价值和经济意义而闻名，但特别容易受到柑橘黄龙病、黑斑病和柑橘溃疡病等病害的影响。由于技术进步，图像处理和深度学习算法现在可以早期检测和分类植物病害，这有助于保护作物健康和产量。所提出的工作使农民能够识别和可视化影响柑橘类植物的多种病害。本研究提出了一种高效模型，用于检测可能同时出现在同一片叶子上的多种柑橘病害（溃疡病、黑斑病和黄龙病）。这是通过RSL（残差挤压与激励泄漏整流线性单元）链接变换网络多类分割模型实现的。所提出的模型在解决现有模型的主要局限性方面表现突出，包括空间不一致性、精细病害边界的丢失以及特征表示不足。所提出的RSL链接变换网络模型的意义在于其集成了分层特征提取、通过变压器进行全局上下文建模以及精确的特征重建，确保了卓越的分割精度和鲁棒性。所提出的RSL链接变换网络架构的结果显示，准确率平均值为0.9755，损失值为0.0660，精确率为0.9779，召回率为0.9738，交并比为0.9308。此外，对于测试数据集中图像的每个病害类别，该模型的平均F1分数为0.7173，平均交并比为0.7567。分割结果进一步用于识别影响叶片的主要病害，并使用主要病害分类和严重程度检测算法评估病害严重程度。
