Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering.

The maturity of kiwifruit is widely gauged by its soluble solids content (SSC), with accurate assessment being essential to guarantee the fruit's quality. Hyperspectral imaging offers a non-destructive alternative to traditional destructive methods for SSC evaluation, though its efficacy is often hindered by the redundancy and external disturbances of spectral images. This study aims to enhance the accuracy of SSC predictions by employing feature engineering to meticulously select optimal spectral features and mitigate disturbance effects. We conducted a comprehensive investigation of four spectral pre-processing and nine spectral feature selection methods, as components of feature engineering, to determine their influence on the performance of a linear regression model based on ordinary least squares (OLS). Additionally, the stacking generalization technique was employed to amalgamate the strengths of the two most effective models derived from feature engineering. Our findings demonstrate a considerable improvement in SSC prediction accuracy post feature engineering. The most effective model, when considering both feature engineering and stacking generalization, achieved an RMSEp of 0.721, a MAPEp of 0.046, and an RPDp of 1.394 in the prediction set. The study confirms that feature engineering, especially the careful selection of spectral features, and the stacking generalization technique are instrumental in bolstering SSC prediction in kiwifruit. This advancement enhances the application of hyperspectral imaging for quality assessment, offering benefits that extend across the agricultural industry.

A Novel Framework of Manifold Learning Cascade-Clustering for the Informative Frame Selection.

Narrow band imaging is an established non-invasive tool used for the early detection of laryngeal cancer in surveillance examinations. Most images produced from the examination are useless, such as blurred, specular reflection, and underexposed. Removing the uninformative frames is vital to improve detection accuracy and speed up computer-aided diagnosis. It often takes a lot of time for the physician to manually inspect the informative frames. This issue is commonly addressed by a classifier with task-specific categories of the uninformative frames. However, the definition of the uninformative categories is ambiguous, and tedious labeling still cannot be avoided. Here, we show that a novel unsupervised scheme is comparable to the current benchmarks on the dataset of NBI-InfFrames. We extract feature embedding using a vanilla neural network (VGG16) and introduce a new dimensionality reduction method called UMAP that distinguishes the feature embedding in the lower-dimensional space. Along with the proposed automatic cluster labeling algorithm and cost function in Bayesian optimization, the proposed method coupled with UMAP achieves state-of-the-art performance. It outperforms the baseline by 12% absolute. The overall median recall of the proposed method is currently the highest, 96%. Our results demonstrate the effectiveness of the proposed scheme and the robustness of detecting the informative frames. It also suggests the patterns embedded in the data help develop flexible algorithms that do not require manual labeling.

Multi-objective optimization method for reducing mutual interference in cockpit illumination.

The cockpit is a compact space comprised of various light-emitting devices. The light from different devices interferes and overlaps on the target surface. The light distribution requirements of different target surfaces are different. A suitable decision-making process is required to simultaneously meet the requirements of multiple target surfaces. A GPR-NSGA-II framework was proposed in the present study and a corresponding Gaussian process regression prediction model was established to predict and optimize multiple optical quality parameters in the cockpit. The luminous flux and beam angle of the typical luminaires were selected as controlled input parameters in a model case. The average illumination of targets that need lighting were set as constraints, and uniformity of illuminance of these surfaces and vertical illumination (direct light) of the eye position were set as the variables. An orthogonal experiment was conducted using the lighting model and a dataset was generated to validate the proposed framework. The results demonstrate that the solution set of luminescence parameters in cockpit illumination can be specified by GPR-NSGA-II framework.