Combining disease categories and crop species leads to complex intra-class and inter-class differences. Significant intra-class difference and subtle inter-class difference pose a great challenge to high-precision crop disease classification tasks. To this end, we propose a multi-granularity feature aggregation method for accurately identifying disease types and crop species as well as better understanding the disease-affected regions implicitly. Specifically, in order to capture fine-grained discriminating clues to disease categories, we first explored the pixel-level spatial self-attention to model the pair-wise semantic relations. Second, we utilized the block-level channel self-attention to enhance the feature-discriminative ability of different crop species. Finally, we used a spatial reasoning module to model the spatial geometric relationship of the image patches sequentially, such that the feature-discriminative ability of characterizing both diseases and species is further improved. The proposed model was verified on the PDR2018 dataset, the FGVC8 dataset, and the non-lab dataset PlantDoc. Experimental results demonstrated our method reported respective classification accuracies of 88.32%, 89.95%, and 89.75% along with F1-scores of 88.20%, 89.24%, and 89.13% on three datasets. More importantly, the proposed architecture not only improved the classification accuracy but also promised model efficiency with low complexity, which is beneficial for precision agricultural applications.

Differential features are popularly used in computer vision tasks, such as object detection. In this paper, we revisit these features from a functional approximation perspective. In particular, we view an image as a 2-D functional and investigate its Taylor series approximation. Differential features are derived from the approximation coefficients and, therefore, are naturally collected for appearance representation. Thus motivated, we propose to use the zeroth-, first-, and second-order differential features for pedestrian detection and call such features Taylor feature transform (TAFT). In practice, the TAFT features are computed by discrete sampling to address scale issues and meanwhile achieve computational efficiency. In addition, orientation insensitivity is handled by using directional versions of differentials. When applied to pedestrian detection, the TAFT is sampled on grid pixels and calculated from multiple channels following previous solutions. In our extensive experiments on the INRIA, Caltech, TUD-Brussel, and KITTI data sets, the TAFT achieves state-of-the-art results. It outperforms all handcrafted features and performs on par with many deep-learning solutions. Moreover, when a low false-positive rate is requested, the TAFT generates results that are better than or comparable to the state-of-the-art deep learning-based methods. Meanwhile, our implementation runs at 33 fps for 640x480 images without GPU, making TAFT favorable in many practical scenarios.