Tomato, as the vegetable queen, is cultivated worldwide due to its rich nutrient content and unique flavor. Nondestructive technology provides efficient and noninvasive solutions for the quality assessment of tomatoes. However, processing the substantial datasets to achieve a robust model and enhance detection performance for nondestructive technology is a great challenge until deep learning is developed. The aim of this paper is to provide a systematical overview of the principles and application for three categories of nondestructive detection techniques based on mechanical characterization, electromagnetic characterization, as well as electrochemical sensors. Tomato quality assessment is analyzed, and the characteristics of different nondestructive techniques are compared. Various data analysis methods based on deep learning are explored and the applications in tomato assessment using nondestructive techniques with deep learning are also summarized. Limitations and future expectations for the quality assessment of the tomato industry by nondestructive techniques along with deep learning are discussed. The ongoing advancements in optical equipment and deep learning methods lead to a promising outlook for the application in the tomato industry and agricultural engineering.

Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions. This study aims to explore a rapid detection method for protein content in maize grain powder based on near-infrared spectroscopy. A method for determining protein content in maize grain powder was established using near-infrared (NIR) reflectance spectra in the 940–1660 nm range. Various preprocessing techniques, including Savitzky−Golay (S−G), multiplicative scatter correction (MSC), standard normal variate (SNV), and the first derivative (1D), were employed to preprocess the raw spectral data. Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. Feature wavelengths were selected to enhance model accuracy further using the Successive Projections Algorithm (SPA) and Uninformative Variable Elimination (UVE). Experimental results indicated that the PLSR model, preprocessed with 1D + MSC, yielded the best performance, achieving a root mean square error of prediction (RMSEP) of 0.3 g/kg, a correlation coefficient (R_p) of 0.93, and a residual predictive deviation (RPD) of 3. The associated methods and theoretical foundation provide a scientific basis for the quality control and processing of maize.

Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. A new dataset of high moisture content maize kernel phenotypic features was constructed by extracting seven features (geometric and shape features). Then, the regression model of the kernel (broken and unbroken) weight prediction and the classification model of kernel defect detection were established using the mainstream machine learning algorithm. In this way, the defect rapid identification and accurate weight prediction of broken kernels achieve the purpose of broken rate quantitative detection. The results prove that LGBM (light gradient boosting machine) and RF (random forest) algorithms were suitable for constructing weight prediction models of broken and unbroken kernels, respectively. The r values of the models built by the two algorithms were 0.985 and 0.910, respectively. SVM (support vector machine) algorithms perform well in constructing maize kernel classification models, with more than 95% classification accuracy. A strong linear relationship was observed between the predicted and actual broken rates. Therefore, this method could help to be an accurate, objective, efficient broken rate online detection method for maize harvest.

To improve the intelligence level of particleboard inspection lines, machine vision and artificial intelligence technologies are combined to replace manual inspection with automatic detection. Aiming at the problem of missed detection and false detection on small defects due to the large surface width, complex texture and different surface defect shapes of particleboard, this paper introduces image super-resolution technology and proposes a super-resolution reconstruction model for particleboard images. Based on the Transformer network, this model incorporates an improved SRResNet (Super-Resolution Residual Network) backbone network in the deep feature extraction module to extract deep texture information. The shallow features extracted by conv 3 × 3 are then fused with features extracted by the Transformer, considering both local texture features and global feature information. This enhances image quality and makes defect details clearer. Through comparison with the traditional bicubic B-spline interpolation method, ESRGAN (Enhanced Super-Resolution Generative Adversarial Network), and SwinIR (Image Restoration Using Swin Transformer), the effectiveness of the particleboard super-resolution reconstruction model is verified using objective evaluation metrics including PSNR, SSIM, and LPIPS, demonstrating its ability to produce higher-quality images with more details and better visual characteristics. Finally, using the YOLOv8 model to compare defect detection rates between super-resolution images and low-resolution images, the mAP can reach 96.5%, which is 25.6% higher than the low-resolution image recognition rate.

This study aims to develop a rapid and non-destructive method for determining protein content in maize using near-infrared spectroscopy (NIRS). To mitigate the effects of surface irregularities and uneven protein distribution in whole kernels on spectral measurements, maize powder was used as the test material to enhance the uniformity and stability of spectral signals. A total of 90 maize powder samples were collected from major production regions across China, and a custom NIRS acquisition system was constructed. To optimize the spectral data, eight preprocessing methods—including Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), First Derivative (1D), Savitzky–Golay smoothing (S–G), and their combinations—were systematically evaluated. Subsequently, traditional machine learning models (Partial Least Squares Regression, PLSR; Support Vector Machine, SVM) and deep learning models (ResNet-18, Transformer) were developed to predict protein content, and their performances were compared. Results indicated that the combined preprocessing strategy of First Derivative and Multiplicative Scatter Correction (1D + MSC) was the most effective. Among the models, PLSR demonstrated the best predictive performance, and traditional chemometric methods showed greater practical utility compared to deep learning models. To further enhance model efficiency, four feature wavelength selection methods—Partial Least Squares Regression Coefficients (PLSRC), Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and Uninformative Variable Elimination (UVE)—were applied. It was found that the PLSR model combined with the Successive Projections Algorithm (SPA) yielded the optimal performance, achieving a validation set correlation coefficient ( R _p ) of 0.927, a root mean square error of prediction (RMSE _P ) of 0.301, and a residual predictive deviation (RPD) of 2.502, along with the fastest computational speed. This study provides a reliable technical solution and theoretical foundation for the rapid and non-destructive detection of protein content in maize, while also validating the advantage of using powdered samples in improving the accuracy of NIRS detection.

Complex harvesting environments and varying crop conditions often lead to threshing cylinder blockage and increased entrainment loss in maize grain harvesters. To address these issues, an electric-driven automatic control system for maize threshing concave clearance based on real-time entrainment loss monitoring was developed. The system automatically adjusts concave clearance parameters at different harvesting speeds to maintain grain entrainment loss within an optimal range. First, an adjustable concave structure based on a crank-link mechanism was designed, with a threshing clearance adjustment range of 15–47 mm and motor rotation angle of 0–48°. Subsequently, an EDEM simulation model of the mixed material discharge inside the threshing cylinder was established to determine the optimal installation position of the entrainment loss monitoring sensor based on piezoelectric ceramic-sensitive elements. The sensor was positioned at the left tail end of the concave sieve, with a minimum distance of 58 mm between the sensitive plate centerline and threshing concave sieve and an installation angle of 65° relative to the horizontal plane. A maize threshing clearance control method based on fuzzy neural network PID control algorithm was proposed, and Simulink simulation optimization verified its superior performance with fast response speed. After system integration, field trials were conducted at low, medium, and high operating speeds with preset ideal entrainment loss intervals. The results showed that control was unnecessary at low speed, the control system-maintained entrainment loss within set range at medium speed, and maximum threshing clearance was needed at high speed. Finally, comparative trials of threshing performance with and without the control system were conducted at medium harvesting speed. Results showed that the entrainment loss rate decreased by 43.75% with the control system activated, significantly reducing maize threshing entrainment losses. This study overcame the barrier of maize threshing parameter adjustment being heavily reliant on manual experience and provided theoretical support for the intelligent grain harvesting equipment.