Drought stress is one of the key abiotic stresses affecting plant growth, crop yield and food quality. The main objective of this study is to investigate the potential effectiveness of hyperspectral imaging with band selection method for the rapid detection of the early drought stress of tomatoes. First, the unsupervised algorithm -K-means and statistical histogram are used to extract samples representing each experimental treatment group. Then, to solve problems related to the high redundancy and correlation of hyperspectral data, band matrix reduction method (BMRM) based on recursive feature elimination theory is proposed to determine the optimal band subset. The band matrix is constructed according to the band ranking obtained by the discrimination coefficient - ( )Coef i , which is calculated from the average spectral curve and the first-derivative spectrum. Finally, the effectiveness of waveband selection algorithms was validated by comparison with successive projections algorithm, competitive adaptive reweighted sampling, recursive feature elimination with cross-validation and full spectrum. The results demonstrated that BMRM achieved higher classification accuracy with fewer bands selected, and the amount of calculation is not greatly improved. The proposed method provides a more accurate, and effective way of detecting early drought stress.
Background Diffusion tensor imaging (DTI), diffusion spectrum imaging (DSI) and Q-ball imaging (QBI) are currently three main diffusion MRI (dMRI) schemes available for non-invasive investigation of cardiac fiber architecture. Although DSI and QBI have undoubtedly greater potential to reveal complex cardiac fiber structures than DTI, it however remains unclear to which level and at which scale they provide more gain for investigating cardiac fiber structure.Method This work attends to provide a quantitative description of cardiac fiber architecture derived from different schemes at various scales. Due to the limit of the spatial resolution of clinical MRI scanner and with the absence of the ground-truth, it is difficult to give the accurate description. To deal with this issue, we simulate firstly DTI, DSI and QBI of a cardiac fiber model with the structure a priori known at different scales, and then the estimation accuracy, the diffusion metrics and the helix and transverse angles of cardiac fiber obtained by different schemes at different scales are calculated. Results The results show that although DSI and QBI can distinguish multiple fiber orientations, they are readily to generate false positive and false negative fibers which influence therefore the estimation accuracy. When there are multiple fiber orientations in one voxel, the diffusion anisotropy detected by DTI is higher than DSI and QBI, the range of helix and transverse angle decreases with increasing of the scales, and that detected by DSI is larger than DTI and QBI. Conclusion The results showed that the proposed dMRI simulator provides a valuable tool for simulating realistic DW images of whole human hearts, which can be used as the gold standard to study the fiber structures of the heart.
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