The bean leaves can be affected by several diseases, such as angular leaf spots and bean rust, which can cause big damage to bean crops and decrease their productivity. Thus, treating these diseases in their early stages can improve the quality and quantity of the product. Recently, several robotic frameworks based on image processing and artificial intelligence have been used to treat these diseases in an automated way. However, incorrect diagnosis of the infected leaf can lead to the use of chemical treatments for normal leaf thereby the issue will not be solved, and the process may be costly and harmful. To overcome these issues, a modern deep learning framework in robot vision for the early detection of bean leaves diseases is proposed. The proposed framework is composed of two primary stages, which detect the bean leaves in the input images and diagnosing the diseases within the detected leaves. The U-Net architecture based on a pre-trained ResNet34 encoder is employed for detecting the bean leaves in the input images captured in uncontrolled environmental conditions. In the classification stage, the performance of five diverse deep learning models (e.g., Densenet121, ResNet34, ResNet50, VGG-16, and VGG-19) is assessed accurately to identify the healthiness of bean leaves. The performance of the proposed framework is evaluated using a challenging and extensive dataset composed of 1295 images of three different classes (e.g., Healthy, Angular Leaf Spot, and Bean Rust). In the binary classification task, the best performance is achieved using the Densenet121 model with a CAR of 98.31%, Sensitivity of 99.03%, Specificity of 96.82%, Precision of 98.45%, F1-Score of 98.74%, and AUC of 100%. The higher CAR of 91.01% is obtained using the same model in the multi-classification task, with less than 2 s per image to produce the final decision.

豆类叶子可能受到多种病害的影响，例如角斑病和豆锈病，这些病害会对豆类作物造成很大损害并降低其生产力。因此，在这些疾病的早期阶段进行治疗可以提高产品的质量和数量。最近，一些基于图像处理和人工智能的机器人框架已被用于以自动化方式治疗这些疾病。然而，对受感染叶子的错误诊断可能会导致对正常叶子使用化学处理，从而无法解决问题，并且该过程可能成本高昂且有害。为了克服这些问题，提出了一种用于早期检测豆叶病害的机器人视觉现代深度学习框架。所提出的框架由两个主要阶段组成，它们检测输入图像中的豆叶并诊断检测到的叶子内的疾病。基于预训练 ResNet34 编码器的 U-Net 架构用于检测在不受控制的环境条件下捕获的输入图像中的豆叶。在分类阶段，准确评估五种不同深度学习模型（例如Densenet121、ResNet34、ResNet50、VGG-16和VGG-19）的性能，以识别豆叶的健康状况。使用具有挑战性且广泛的数据集来评估所提出的框架的性能，该数据集由三个不同类别（例如，健康、角斑病和豆锈病）的 1295 个图像组成。在二元分类任务中，使用Densenet121模型取得了最佳性能，CAR为98.31%，Sensitivity为99.03%，Specificity为96.82%，Precision为98.45%，F1-Score为98.74%，AUC为100%。较高的 CAR 为 91。01% 是在多分类任务中使用相同的模型获得的，每张图像产生最终决策的时间少于 2 秒。