The optical properties of fresh maize tissues determine how light interacts with fresh maize cobs, which in turn affects the measured spectral signals and model accuracy. In this paper, a simulation model was developed to invert the optical properties of fresh maize cobs and evaluate the effects of different optical layouts on the accuracy of modeling predictions. First, the uniformity of detector irradiation at various distances (10 mm, 20 mm, 30 mm, 40 mm, 50 mm) and angles (30°, 45°, 60°) with different optical properties was analyzed using optical simulation methods. Then, the spectra of fresh maize cobs were collected at different light source angles and detection distances, and the spectral area polarization was calculated. Finally, the optical properties of the cob were estimated by establishing a link between irradiation uniformity and spectral area polarization, which resolved the distribution of light flux in edible maize cobs under different optical structures. The results show that the model of light transport mimicking the organizational structure of maize cob has been successfully simulated. The estimated optical properties of the cob are: absorption A=37%, transmission T=20%, and diffuse reflectance D=40%. This verifies that the accuracy and precision of the prediction model for the water content of fresh maize are best achieved under an optical structure with a detection distance of 40 mm and a light source angle of 45°. The establishment of the simulation model provides theoretical support for near-infrared detection of the intrinsic quality of fresh maize.
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This study aims to accurately and rapidly detect the fresh corn cobs in modern agriculture. A lightweight model was also proposed for the superior and inferior fresh corn cobs using the improved SS-YOLOv8. Among them, the YOLOv8 model was one of the five size versions in the YOLO series. Furthermore, the YOLOv8n also shared the most lightweight architecture and the high detection speed for real-time tasks. In addition, the high accuracy of the YOLOv8 was comparable to the advanced target detection. Compared with the traditional ones, the YOLOv8 model was the best choice to serve as the base model in the end-to-end detection with significantly higher speeds. Therefore, the quality of the corn cob was evaluated after detection. Firstly, the backbone of the network was improved for the feature extraction, where a large number of the kernels were independently and tightly arranged on the fresh corn cob. The loss of feature information was avoided to reduce the number using feature reuse lightweight network, ShuffleNetV2, the lightweight convolutional layer (SPD-Conv) with the fine-grained information, and the maximally-pooled convolutional layer (Conv_Maxpool). Secondly, a simple and parameter-free attention module (SimAM) was integrated into the backbone network of the feature extraction. The network was enhanced to extract the features from the defective corn cobs. Finally, the Wise-IoU (WIoU) was introduced as the regression loss function of bounding box for the YOLOv8n. The accuracy of the identification was further improved for the flaws of the corn cob. The convergence was also promoted during training. The experimental results show that the improved SS-YOLOv8 model effectively detected the qualified and unqualified fresh corn. The mean average precision (mAP) was achieved at 98.7%, which was 1.4 percentage points higher than the baseline model; The number of parameters and model size were only 1.99 M and 4.2 MB, respectively, which were reduced to 66.1% and 66.7% of the baseline model. Additionally, the various attention mechanisms (such as the SimAM, CBAM, and SE) were introduced into the feature extraction network. Among them, the Sim attention mechanism shared the best performance. The regression loss functions of the bounding box were assessed on the performance of the improved model. The WIoU outperformed GIoU, DIoU, and CIoU in the speed of convergence and the size of loss values. A systematic comparison was also made on the two-stage model, Faster R-CNN, the single-stage model, SSD, and the YOLOv5, v6, and v8 of the YOLO series. The results show that the precision, recall, and mAP of the SS-YOLOv8 model were much higher than those of the Faster R-CNN and SSD models, while slightly higher than those of the YOLOv8 and YOLOv5 models. In addition, the improved model was far better than the rest in terms of the computational parameters and size. In summary, the improved SS-YOLOv8 model shared significantly better performance in detection accuracy, the number of parameters, and model size, compared with the rest. As such, the SS-YOLOv8 model can be expected to realize the effective identification of the qualified and unqualified corn cobs with small weights and a number of computational parameters. The finding can provide a strong reference for the detection equipment of the maize grading.
Fresh corn is increasingly an important choice in the daily diet of consumers due to its rich nutrition and sweet taste. With the improvement of living standards, people's quality requirements for fresh corn continue to improve, among which amylose content is a key indicator affecting the taste and flavor of corn, at present, the industry mainly uses chemical detection methods to determine amylose content, which is not only time-consuming and laborious, destroys samples, but also difficult to meet the needs of rapid detection in modern agricultural production and food processing. Therefore, the development of an efficient, accurate and non-destructive rapid detection technology for amylose has become a key issue in the field of agricultural product quality control.
In this study, a non-destructive detection model for amylose content in ears of fresh corn based on near-infrared spectroscopy technology was established. Taking Jinguan 597 fresh corn as the research object, the near-infrared spectroscopic detection system independently built by the laboratory was used to collect diffuse reflectance spectral data in the middle area of the complete corn ear to ensure that the detection process did not damage the integrity of the sample. At the same time, the physical and chemical values of amylose content in samples were determined with reference, and a standard database was established. In the data preprocessing stage, the Mahalanobis Distance method was used to screen the outliers of the original spectral data, and the abnormal samples caused by operating errors or sample defects were eliminated, and finally 90 representative fresh corn samples were retained for modeling analysis. In order to optimize the model performance, the effects of five mainstream spectral pretreatment methods were compared: standard normal variable (SNV) transform to eliminate the influence of optical path difference, multiplicative scatter correction (MSC) to reduce particle scattering interference, SavitZky-Golay smoothing (SGS) to remove random noise, first-order derivative (FD) to enhance spectral characteristic peaks, and detrending (DT) to eliminate baseline drift. Based on the partial least squares regression (PLSR) algorithm, a full-band amylose prediction model was constructed, and the robustness of the model was evaluated by cross-validation. In order to further improve the efficiency of model operation, the characteristic wavelengths with the strongest correlation with amylose content were selected from the whole spectrum by innovatively combining two variable selection methods, competitive adaptive reweighted sampling (CARS) and continuous successive projections algorithm (SPA), and a simplified characteristic band prediction model was established.
The results demonstrated that among the various combined models incorporating different preprocessing and feature wavelength selection methods, the "SNV-CARS-PLSR" model, which integrated SNV preprocessing with CARS feature extraction, exhibited superior performance. This model significantly outperformed alternative modeling approaches in predictive capability. The model achieved the following performance metrics: a calibration coefficient of determination (RC2) of 0.826, root mean square error of calibration (RMSEC) of 1.399, prediction coefficient of determination (RP2) of 0.820, root mean square error of prediction (RMSEP) of 1.081, and residual predictive deviation (RPD) of 2.426. Comparative analysis revealed that the "SNV-CARS-PLSR" model showed a 14.0% improvement in RP2 compared to the full-band PLSR model with SNV preprocessing alone. This enhancement was primarily attributed to the CARS algorithm's effective identification of key feature wavelengths. Through its adaptive weighting and iterative optimization process, CARS successfully extracted 22 characteristic wavelengths that were strongly correlated with amylose content from the original 157 wavelength points in the full spectrum. This selective extraction process effectively eliminated redundant spectral information and noise interference, thereby significantly improving the model's predictive accuracy.
Combined SNV preprocessing with CARS feature selection, the study successfully established a rapid, non-destructive prediction model for amylose content in fresh maize ears utilizing near-infrared spectroscopy technology. The developed methodology demonstrated significant advantages, including rapid analysis capability and complete non destructiveness of samples. The reseach could provide technical support for rapid, non-destructive detection of amylose in fresh maize ears.
This study aims to obtain the high-quality near-infrared (NIR) spectra of fresh corn cobs. A systematic investigation was made to explore the effects of experimental parameters on the spectral features of fresh corn cobs and modeling validation using NIR diffuse reflectance spectroscopy. According to the cob stick-shaped characteristics, the multi-dimensional comprehensive test was carried out to collect 900~1700 nm spectral data under four parameters, namely, light source type, light intensity, detection distance, and light source angle. The halogen lamp cups were used as the fiber optic light sources. Among them, the halogen lamp cups were selected with the power of 20 and 40 W, detection distances of 10 and 50 mm, and halogen lamp cups with angles of 30°, 45°, and 60°, in order to analyze the spectral differences and distribution patterns of curves. The spectral differences were determined for the distribution patterns. The standard deviation and spectral area extreme deviation indexes were used to evaluate the spectral quality. Further validation tests were carried out on the model. The spectra were evaluated at 30° and 45° halogen lamp cup angles by multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative (FD), and trend correction. Furthermore, the new model was established to predict the water content using partial least squares (PLS) and support vector machines (SVM). The performance of the model was compared after the derivative (FD) and detrending (DT) pre-processing. The experimental results showed that sufficient spectral response and less interference were achieved in the halogen lamp cup with a power of 20 W and a detection distance of 40 mm. The standard deviation and spectral area polarity of the curve were 0.83 and 187.2, respectively. The better quality of the spectral curve and higher performance of the model were also obtained in the halogen lamp cup with the clamp angle of 45°, compared with 30°. The better performance was found in the SVM prediction model after SNV preprocessing. The coefficients of determination were 0.943 and 0.880, respectively, in the correction and prediction datasets, while the root mean square errors were 0.708 and 0.932, respectively, and the residual prediction deviation was 2.956. The finding can provide technical support to the nondestructive test on the intrinsic quality of fresh corn cobs using near-infrared diffuse reflectance spectroscopy.
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