Abstract The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy estimates of the “truth” under the influence of their varying skill‐levels and biases. Blindly treating these noisy labels as the ground truth limits the accuracy of learning algorithms in the presence of strong disagreement. This problem is critical for applications in domains where the annotation cost is high. Such problems are extremely serious in the domain of agricultural imaging in leaf disease classification. Due to the limitation of acquisition methods, noisy examples are often not only the normal mislabelled ones, but also the samples which contain more than one instance. To cope with the combination of the two noises, label smoothing is blended in the point of increasing entropy for uncertain labels, with Taylor cross entropy loss, which is proved to be efficient to solve the problem of artificial noisy labels on public datasets. And the proposed method is called smooth‐Taylor cross entropy loss, which can deal with the real‐world noises in vision leaf disease dataset. Extensive experimental results on cassava leaf disease dataset demonstrate that our proposed approach significantly outperforms the state‐of‐the‐art counterparts.

Aiming at the problem that optical coherence tomography (OCT) images with low contrast and layer structure blur are difficult to be automatically layered, a new OCT detection method based on complex shearlet transform is proposed. The method utilises nearly optimal sparse approximation singular curves of multi‐scale shearlet transform, and the contrast invariance of the phase congruence method. Compared with the Canny edge detector and wavelet methods, the complex shearlet‐based method achieved the highest Pratt figure of merit (PFOM) value. The PFOM value of a step type edge is 0.92, and that of a pulse type edge is 0.98. Three types of OCT images were tested, including normal retinal macula area, dry age‐related macular degeneration, and Stargardt disease. The experimental results show that the complex shearlet‐based method can detect more layered structures of OCT images, especially the boundary between the ganglion cell layer and the inner plexiform layer that is difficult to detect, and it can detect various types of OCT images. The complex shearlet‐based transform method provides an effective and general way to measure retinal OCT images.