The agricultural crops, like corn, suffer from the presence of undesirable plants known as weeds, which compete for sunlight and water, leading to lower crop yields. Recognizing weeds during their early growth stage is vital for minimizing their impact on crop growth and maximizing yield. By leveraging a lightweight deep neural network, this research endeavours to classify corn and the weeds that often grow alongside it. To achieve this, the Enhanced Convolutional Block Attention Module (CBAM) embedded EfficientNet model (ECENet) is proposed by integrating the enhanced CBAM with EfficientNetB0 model and the inclusion of extra layers. The Enhanced CBAM has been created by modifying the original CBAM through the parallel arrangement of the Channel Attention Module (CAM) and Spatial Attention Module (SAM). The simultaneous use of attention modules eradicates the need for CAM and SAM to be dependent on each other, resulting in the independent extraction of attention feature maps. The ECENet model was trained and tested on the corn weed dataset to understand the discriminative features of corn and weed. The proposed system yielded 99.92% overall recognition accuracy, with 4,772,010 parameter footprints, a model size of 57.4 megabytes, and 0.796 giga floating-point operations per second (GFLOPs). The proposed ECENet takes 37%, 91%, 80%, and 78% fewer parameters than DenseNet121, InceptionResNetV2, ResNet50V2, and XceptionNet respectively. The proposed model excels in diagnosing weed and crop differentiation, outperforming previous studies and state-of-the-art models. Finally, interpretability of the proposed model has been provided using explainable AI techniques (XAI) such as GradCAM and LIME. Due to its small memory requirement and high accuracy, the ECENet is ideal for real-time corn and weed classification on handy and mobile devices with minimal computational capabilities. The system can also be expanded to be included in agricultural robots for real-world weeding in large farmlands.

中文翻译：

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使用 Explainable AI 技术支持的改进注意力机制对玉米进行基于深度学习的杂草分类


像玉米一样的农作物存在被称为杂草的不良植物，这些植物会争夺阳光和水，导致作物产量下降。在杂草生长的早期阶段识别杂草对于最大限度地减少它们对作物生长的影响和最大限度地提高产量至关重要。通过利用轻量级深度神经网络，这项研究致力于对玉米和通常与其一起生长的杂草进行分类。为了实现这一目标，通过将增强的 CBAM 与 EfficientNetB0 模型集成并包含额外的层，提出了增强型卷积块注意力模块 （CBAM） 嵌入式 EfficientNet 模型 （ECENet）。增强型 CBAM 是通过通道注意力模块 （CAM） 和空间注意力模块 （SAM） 的并行排列对原始 CBAM 进行修改而创建的。同时使用注意力模块消除了 CAM 和 SAM 相互依赖的需要，从而独立提取了注意力特征图。ECENet 模型在玉米杂草数据集上进行了训练和测试，以了解玉米和杂草的区分特征。所提出的系统产生了 99.92% 的整体识别准确率，具有 4,772,010 个参数占用空间，模型大小为 57.4 MB，每秒浮点运算 （GFLOP） 为 0.796 GB。所提出的 ECENet 比 DenseNet121、InceptionResNetV2、ResNet50V2 和 XceptionNet 分别少了 37%、91%、80% 和 78% 的参数。所提出的模型在诊断杂草和作物分化方面表现出色，优于以前的研究和最先进的模型。最后，使用 GradCAM 和 LIME 等可解释的 AI 技术 （XAI） 提供了所提出的模型的可解释性。 由于其内存要求小且准确性高，ECENet 非常适合在计算能力最少的便携式和移动设备上进行实时玉米和杂草分类。该系统还可以扩展为包含在农业机器人中，用于在大型农田中进行实际除草。