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Predicting the crystal structure is essential to address the reliance on serendipity for facilitating the discovery and design of high‐performance high‐entropy oxides (HEOs). Here, three classic algorithms‐based machine learning models to predict the crystal structure of HEOs are successfully established and analyzed by combining five metrics, and the XGBoost classifier shows excellent accuracy and robustness with ACC and F1 scores up to 0.977 and 0.975, respectively. SHAP summary plot indicates that the anion‐to‐cation radius ratio (rA/rC) has the greatest impact on crystal structure, followed by difference in Pauling and Mulliken electronegativities (ΔχPauling and ΔχMulliken). It is noteworthy that the rA/rC, ΔχPauling, and ΔχMulliken lower than 0.35, 0.1, and 0.2, respectively, tend to lead to a fluorite crystal structure, whereas rock‐salt and spinel crystal structures are always formed. This work is expected to facilitate the discovery and design of HEOs with tailorable crystal structures and properties. [ABSTRACT FROM AUTHOR]

Accurate soil quality evaluation is an important prerequisite for improving soil management systems and remediating soil pollution. However, traditional soil quality evaluation methods are cumbersome to calculate, and suffer from low efficiency and low accuracy, which often lead to large deviations in the evaluation results. This study aims to provide a new and accurate soil quality evaluation method based on graph convolution network (GCN). In this study, soil organic matter (SOM), alkaline hydrolysable nitrogen (AN), available potassium (AK), salinity, and heavy metals (iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn)) were determined and evaluated using the soil quality index (SQI). Then, the graph convolution network (GCN) was first introduced in the soil quality evaluation to construct an evaluation model, and its evaluation results were compared with those of the SQI. Finally, the spatial distribution of the evaluation results of the GCN model was displayed. The results showed that soil salinity had the largest coefficient of variation (86%), followed by soil heavy metals (67%) and nutrients (30.3%). The soil salinization and heavy metal pollution were at a low level in this area, and the soil nutrients and soil quality were at a high level. The evaluation accuracy of the GCN model for soil salinity/heavy metals, soil nutrients, and soil quality were 0.91, 0.84, and 0.90, respectively. Therefore, the GCN model has a high accuracy and is feasible to be applied in the soil quality evaluation. This study provides a new, simple, and highly accurate method for soil quality evaluation. [ABSTRACT FROM AUTHOR]

Cotton is one of the most important cash crops in Xinjiang, and timely seedling inspection and replenishment at the seedling stage are essential for cotton's late production management and yield formation. The background conditions of the cotton seedling stage are complex and variable, and deep learning methods are widely used to extract target objects from the complex background. Therefore, this study takes seedling cotton as the research object and uses three deep learning algorithms, YOLOv5, YOLOv7, and CenterNet, for cotton seedling detection and counting using images at six different times of the cotton seedling period based on multispectral images collected by UAVs to develop a model applicable to the whole cotton seedling period. The results showed that when tested with data collected at different times, YOLOv7 performed better overall in detection and counting, and the T4 dataset performed better in each test set. Precision, Recall, and F1-Score values with the best test results were 96.9%, 96.6%, and 96.7%, respectively, and the R2, RMSE, and RRMSE indexes were 0.94, 3.83, and 2.72%, respectively. In conclusion, the UAV multispectral images acquired about 23 days after cotton sowing (T4) with the YOLOv7 algorithm achieved rapid and accurate seedling detection and counting throughout the cotton seedling stage. [ABSTRACT FROM AUTHOR]

Simple Summary: Computed tomography angiography can provide sufficient information for the diagnosis of acute aortic syndromes. However, non-contrast computed tomography images in the emergency department, compared with CTA, are more easily accessible and convenient and have lower radiation doses with fewer contraindications. We retrospectively analyzed 325 patients' non-contrast CT images from 2 independent medical centers and established an acute aortic syndrome recognition model based on the radiological features of non-contrast CT images through feature extraction and screening. This model can effectively detect acute aortic syndrome on non-contrast CT images with high sensitivity, AUC, and robustness. More importantly, it can diagnose patients who do not have specific imaging findings on non-contrast CT images. It has important clinical applications for the screening of acute aortic syndrome, especially in the emergency department. We aimed to detect acute aortic syndromes (AAS) on non-contrast computed tomography (NCCT) images using a radiomics-based machine learning model. A total of 325 patients who underwent aortic CT angiography (CTA) were enrolled retrospectively from 2 medical centers in China to form the internal cohort (230 patients, 60 patients with AAS) and the external testing cohort (95 patients with AAS). The internal cohort was divided into the training cohort (n = 135), validation cohort (n = 49), and internal testing cohort (n = 46). The aortic mask was manually delineated on NCCT by a radiologist. Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to filter out nine feature parameters; the Support Vector Machine (SVM) model showed the best performance. In the training and validation cohorts, the SVM model had an area under the curve (AUC) of 0.993 (95% CI, 0.965–1); accuracy (ACC), 0.946 (95% CI, 0.877–1); sensitivity, 0.9 (95% CI, 0.696–1); and specificity, 0.964 (95% CI, 0.903–1). In the internal testing cohort, the SVM model had an AUC of 0.997 (95% CI, 0.992–1); ACC, 0.957 (95% CI, 0.945–0.988); sensitivity, 0.889 (95% CI, 0.888–0.889); and specificity, 0.973 (95% CI, 0.959–1). In the external testing cohort, the ACC was 0.991 (95% CI, 0.937–1). This model can detect AAS on NCCT, reducing misdiagnosis and improving examinations and prognosis. [ABSTRACT FROM AUTHOR]

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI. [ABSTRACT FROM AUTHOR]

Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R2 = 0.80 and RMSE = 1.67 g kg−1 of the Xinluzao 45 optimal model, and R2 = 0.42 and RMSE = 3.13 g kg−1 of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved. [ABSTRACT FROM AUTHOR]

Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376–1044 nm) and near-infrared (NIR) (915–1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes. [ABSTRACT FROM AUTHOR]

Objectives: EGFR testing is a mandatory step before targeted therapy for non-small cell lung cancer patients. Combining some quantifiable features to establish a predictive model of EGFR expression status, break the limitations of tissue biopsy. Materials and Methods: We retrospectively analyzed 1074 patients of non-small cell lung cancer with complete reports of EGFR gene testing. Then manually segmented VOI, captured the clinicopathological features, analyzed traditional radiology features, and extracted radiomic, and deep learning features. The cases were randomly divided into training and test set. We carried out feature screening; then applied the light GBM algorithm, Resnet-101 algorithm, logistic regression to develop sole models, and fused models to predict EGFR mutation conditions. The efficiency of models was evaluated by ROC and PRC curves. Results: We successfully established Modelclinical, Modelradiomic, ModelCNN (based on clinical-radiology, radiomic and deep learning features respectively), Modelradiomic+clinical (combining clinical-radiology and radiomic features), and ModelCNN+radiomic+clinical (combining clinical-radiology, radiomic, and deep learning features). Among the prediction models, ModelCNN+radiomic+clinical showed the highest performance, followed by ModelCNN, and then Modelradiomic+clinical. All three models were able to accurately predict EGFR mutation with AUC values of 0.751, 0.738, and 0.684, respectively. There was no significant difference in the AUC values between ModelCNN+radiomic+clinical and ModelCNN. Further analysis showed that ModelCNN+radiomic+clinical effectively improved the efficacy of Modelradiomic+clinical and showed better efficacy than ModelCNN. The inclusion of clinical-radiology features did not effectively improve the efficacy of Modelradiomic. Conclusions: Either deep learning or radiomic signature-based models can provide a fairly accurate non-invasive prediction of EGFR expression status. The model combined both features effectively enhanced the performance of radiomic models and provided marginal enhancement to deep learning models. Collectively, fusion models offer a novel and more reliable way of providing the efficacy of currently developed prediction models, and have far-reaching potential for the optimization of noninvasive EGFR mutation status prediction methods. [ABSTRACT FROM AUTHOR]

• Data-driven tools for environmental regulation models in protected agriculture. • Independent software platform for processing Pn data and formulating targets. • Functions for Pn prediction, environment regulation model development, and visualization. • Ability to handle photosynthetic data, achieving high precision and low error. Protected agriculture has emerged as a key solution to address the pressing issue of food scarcity. To enhance crop yield, environmental regulation techniques have been widely employed in protected production. However, the absence of user-friendly, data-driven tools for developing regulation models remains a challenge. This study aims to propose IPECM, an independent and user-friendly software platform for processing and analyzing crop photosynthetic rate (Pn) data and formulating environmental regulation targets. The platform provides functionalities, such as Pn prediction model development, environmental regulation model development and result visualization, supporting various machine learning algorithms and regulation target obtaining algorithms. The IPECM Platform's application is demonstrated through examples of light intensity regulation for cucumber growth and CO 2 concentration regulation for tomato growth. The results showcase the software's ability to handle photosynthetic data of any dimension, with the established Pn prediction model achieving a coefficient of determination of 0.98 and a root mean square error lower than 1 μmol·m−2·s−1. The established regulation models can achieve maximum Pn or optimal energy utilization efficiency according to user requirements. IPECM Platform is an independent, automated, and open-source software for protected environmental regulation modeling, providing both the modeling process and results visualization. It offers valuable services for protected agriculture research, eliminating the need for programming knowledge. [ABSTRACT FROM AUTHOR]

• A predictive model of photosynthetic rate was proposed. • U-chord curvature was used to determine knee point of a light-response curve. • A decision-making model of light environment control was proposed for tomato. • The decision-making model could improve Pn with a few energy consumptions. Light, the energy source for crop photosynthesis, is a key factor for plant growth. The present study proposes a decision-making model of light environment control. The photosynthesis rate of tomato seedlings under different light intensities, temperatures, and CO 2 concentrations was determined in a nested experiment. These data were used to construct a predictive model of the photosynthesis rate using the support vector regression method, with an R2 of 0.9862, a root mean square error of 1.39 μmol·m−2·s−1, and a mean absolute error of 1.18 μmol·m−2·s−1. In total, 861 discrete light-response curves were obtained based on the predictive model, and their knee points were computed using the U-chord curvature method. These knee points were used to form a dataset for constructing a decision-making model for light environment control, with an R2 of 0.984 and a root mean square error of 9.55 μmol·m−2·s−1. The results of the validation experiment suggested that the average relative error of the model was 1.92%, indicating the robustness of the model. Compared with those of the light saturation control method, the average light demand for the decision-making model decreased by 60.49%, whereas the average photosynthesis rate reduced by 24.40%. Although the photosynthesis rate lost a bit, the rate of light saving is almost three times more than the rate of photosynthesis rate decreased slightly, which improved the production efficiency of tomato. [ABSTRACT FROM AUTHOR]

Processing tomato (Lycopersicon esculentum Mill.) is rich in vitamins and lycopene, which is favored by consumers. In this study, near-infrared hyperspectral imaging (HSI) technology (980–1660 nm) was used to detect the firmness, soluble solids, lycopene, and titratable acid content of processing tomatoes and to classify fruits at three maturity stages. Savitzky-Golay (SG) smoothing was used to reduce the noise of hyperspectral images. The average spectrum of the tomato fruit was extracted for model development. Random forest (RF), partial least squares (PLS), and recurrent neural network (RNN) were used to develop models for predicting the four quality attributes and identifying the maturity level. Results showed that the RNN model had a classification accuracy of 40% higher than RF and 17% higher than PLS. In the prediction of quality parameters, RNN models had the highest R2 value (>0.87), followed by PLS and RF models. Important wavelengths were identified by calculating its contribution values and were used to interpret the model. The results illustrated that near-infrared hyperspectral imaging technology combined with deep learning could effectively predict the quality and maturity of processing tomatoes. The work can provide a perspective on the application of HSI as a nondestructive testing approach for other agricultural products. • Use of hyperspectral imaging to predict multi-quality of processing tomatoes. • Use of hyperspectral imaging to classify maturity of processing tomatoes. • Comparison of recurrent neural networks with traditional machine learning methods. • Identification of important wavelengths in contributing to model performance. [ABSTRACT FROM AUTHOR]