Cyclobutrifluram is a novel succinate dehydrogenase inhibitor (SDHI) developed by Syngenta and helps to inhibit Fusarium pseudograminearum. Here, the potential for cyclobutrifluram resistance in F. pseudograminearum and the resistance mechanism involved were evaluated. Baseline sensitivity of F. pseudograminearum to cyclobutrifluram was determined with a mean EC 50 value of 0.0248 μg/mL. Fungicide adaption generated five resistant mutants, which possess a comparable or a slightly impaired fitness compared to corresponding parental isolates. This indicates that the resistance risk of F. pseudograminearum to cyclobutrifluram might be moderate. Cyclobutrifluram-resistant isolates also demonstrated resistance to pydiflumetofen but sensitivity to carbendazim, phenamacril, tebuconazole, fludioxonil, or pyraclostrobin. Additionally, point mutations H248Y in FpSdhB and A83V or R86K in FpSdhC 1 were found in cyclobutrifluram-resistant F. pseudograminearum mutants. Molecular docking and overexpression transformation assay revealed that FpSdhB H248Y and FpSdhC 1 A83V or FpSdhC 1 R86K confer the resistance of F. pseudograminearum to cyclobutrifluram.

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The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP) algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

As a recently proposed technique, sparse representation based classifier (SRC) has been widely used for hyperspectral imagery classification and detection. The collaborative representation (CR) and the sparse coding are two key points in SRC scheme. More recently, the proposition that which one of them plays a dominant role in SRC scheme has attracted much attention from researchers in fields of image processing, computer vision, and pattern recognition. In this paper, we first discuss why CR or sparsity works and why one of them alone is not sufficient, and then analyze how CR and sparsity interact with each other. Although we focus on how sparsity augments CR, the necessity of CR for sparsity is also illustrated in both pixel-wise model and joint sparsity model. Inspired by the analysis, we indicate that CR is a powerful tool for solving the high-dimensional pattern recognition with small sample in SRC scheme; sparsity augments CR-based classification in stabilizing, making sure unique solution and rejecting outlying samples. In other words, CR and sparsity complement each other and are both indispensible for hyperspectral imagery classification. The experimental results on simulated data and real hyperspectral imagery confirm the conclusion.

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