Lettuce (Lactuca sativa L.) is an important leafy vegetable with substantial diversity in leaf topology, geometry, color, and texture, which poses challenges for germplasm identification and new variety protection. However, current approaches to complex phenotypic analysis are often limited in their ability to explicitly represent and exploit semantic relationships among phenotypic traits. To address this limitation, a knowledge graph-enhanced graph learning framework for lettuce phenotypic traits was developed. Phenotypic traits were first extracted from leaf images of five lettuce types. Based on the trait description standards of the International Union for the Protection of New Varieties of Plants (UPOV), a Lettuce Leaf Phenotypic Trait Knowledge Graph (LLPT-KG) was constructed to represent semantic associations among traits. On this basis, a Dual-Channel Relational Graph Convolutional Network (DCR-GCN) was developed to jointly integrate node attribute features and graph structural information for lettuce type classification. To improve interpretability, node- and edge-level importance analyses were further performed to identify the phenotypic traits and semantic relations most relevant to type discrimination. The proposed framework achieved an accuracy of 0.94 and a Macro-F1 score of 0.94. Compared with the best-performing single-channel graph baseline, R-GCN (Relational Graph Convolutional Network), DCR-GCN improved accuracy by approximately 9

Daylily is rich in nutrients and has high edible, medicinal, and economic value. It has many producing areas in China. The origin discrimination and soluble protein content prediction of daylilies are of great significance to the quality management of daylilies, the establishment of an agricultural product brand, and the development of the local economy. Because fresh daylily gontains a variety of alkaloids, it is not suitable to eat in large quantities. Therefore, most of the daylilies on the market are dried Aaylilies. In this paper, the origin discrimination model and soluble protein content prediction model for dried daylily were established based on near-infrared spectroscopy. To address the issues of low discrimination accuracy and inaccurate content prediction in the original algorithm, the model was enhanced, resulting in a significant improvement in accuracy through the Combination of various preprocessing methods and characteristic wavelength screening algorithms, In this study, Partial Least Squares Discriminant Analysis (PLS-DA), Random Forest (RF), and Support Vector Machine (SVM) were combined with Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV) and Savitzky-Golay smoothing (SG) respectively to stablish the origin discrimination models of dried daylily and compare the model discrimination results. The experimental results how that PLS-DA combined with MSC has the best effect on origin discrimination, with an accuracy of 93.33%. The precision And recall of the three origins are all above 85%, with an average precision of 91.9% and an average recall of 91.9%. It demonstrates that the model exhibits good accuracy and stability, and can effectively distinguish the origin of dried daylilies. At the same time, Partial Least Squares Regression (PLSR) was combined with a variety of preprocessing methods and three Characteristic wavelength screening algorithms: Unobserved Variable Elimination (UVE), Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA), respectively, to establish the prediction models of soluble Protein content of dried daylily and compare the prediction results. The results show that the model established by PLSR, gombined with SG and CARS, has the best predictive effect. The determination coefficient R reached 0.981 5, and the Root Mean Square Error of Prediction (RMSEP) was 0.021 4 g.kg(-1) Compared with the original PLSR, the R increased by 0. 12, and the RMSEP decreased by 0.033 1 g.kg(-1.) This prediction model can well predict the soluble protein content of dried daylily.

Rapid and non-destructive acquisition of soil nutrient information is crucial for precision fertilization and soil quality monitoring. This study aims to establish a Raman spectroscopy-based framework for predicting key soil fertility indicators, including alkali-hydrolyzable nitrogen (AN), total nitrogen (TN), total phosphorus (TP), and organic matter (OM). The framework systematically integrates three typical spectral preprocessing methods (Standard Normal Variate transformation (SNV), Savitzky–Golay first derivative (SG_D1), and wavelet transform (Wavelet)), three feature selection strategies (Recursive Feature Elimination, XGBoost importance, and Random Forest importance), and 14 mainstream regression models to construct a multi-combination modeling system. Model performance was evaluated using five-fold cross-validation, with 80% of samples used for training and 20% for validation in each fold. Preprocessed Raman spectral features served as input variables, while the corresponding nutrient contents were used as outputs. Results showed significant differences in prediction performance across various combinations of preprocessing methods and regression algorithms for the four soil nutrient indicators. For AN prediction, the combination of Raw_SNV preprocessing with ElasticNet and BayesianRidge models achieved the best performance, with Test R2 values of 0.713 and 0.721, and corresponding Test NRMSE as low as 0.092. For OM prediction, the same Raw_SNV preprocessing with ElasticNet and BayesianRidge also performed well, yielding Test R2 values of 0.825 and 0.832, and Test NRMSE of 0.100 and 0.098, respectively. In TN prediction, both ElasticNet and BayesianRidge under Raw_SNV preprocessing achieved consistent Test R2 of 0.74 and Test NRMSE around 0.20, indicating stable reliability. For TP prediction, the BayesianRidge model with Raw_SNV preprocessing outperformed all others with a Test R2 of 0.71 and Test NRMSE of just 0.089, followed closely by ElasticNet (Test R2 = 0.70, Test NRMSE = 0.092). Overall, the Raw_SNV preprocessing method demonstrated superior performance compared to SG_D1_SNV and Wavelet_SNV. Both BayesianRidge and ElasticNet consistently achieved high R2 and low NRMSE across multiple targets, showcasing strong generalization and robustness, making them optimal model choices for Raman spectroscopy-based soil nutrient prediction. This study demonstrates that Raman spectroscopy, when combined with appropriate preprocessing and modeling techniques, can effectively predict soil organic matter and nitrogen in specific soil types under laboratory conditions. These results provide initial methodological insights for future development of intelligent soil nutrient diagnostics.

To address the challenge of parameter accuracy in discrete element method (DEM) simulations of buckwheat seeds within a seed metering device, this study characterized the physical properties of buckwheat seeds and subsequently calibrated the simulation model parameters. Initially, physical experiments were conducted to determine the triaxial dimensions, angle of repose, and static friction coefficients of buckwheat seeds against stainless steel surfaces. A three-dimensional model of the buckwheat seeds was then generated using Inventor and it was imported into EDEM to simulate the seed stacking process, with the angle of repose quantified via image processing techniques. Employing a Plackett-Burman design, initial parameters were screened, identifying the static friction coefficient between buckwheat seeds, the rolling friction coefficient between buckwheat seeds, and the static friction coefficient between buckwheat seeds and stainless steel as significant factors influencing the angle of repose. The optimal range for these significant parameters was determined through a steepest ascent experiment, and a second-order regression model was developed using Box-Behnken experimental results to optimize the angle of repose and the identified parameters. The optimized parameter set comprised a static friction coefficient between buckwheat seeds and stainless steel of 0.448, a rolling friction coefficient between buckwheat seeds of 0.038, and a static friction coefficient between buckwheat seeds of 0.372. Validation through both simulations and physical experiments revealed a relative error of 1.08%, confirming the reliability of the calibrated parameters for simulating buckwheat seed sowing machinery.

Aiming at the problem of uneven stubble height caused by large topographic relief and scattered plots of buckwheat fields in Hilly and mountainous areas, taking 4SZ-1.5 buckwheat windrower as the research object, an omni-directional header profiling strategy based on fuzzy control was proposed. By constructing the cooperative control system of the rotary lateral profiling mechanism (rotary hydraulic cylinder) and the lifting longitudinal profiling mechanism (lifting hydraulic cylinder), combined with the sliding plate angular displacement sensor, the real-time road elevation information is obtained. The random road excitation is generated by the filtered white noise time-domain model, and the Mamdani type fuzzy controller is established. The hydraulic cylinder expansion and contraction command is output with the left and right angle deviation as the input. Simulink simulation shows that the fuzzy controller can effectively perceive the real-time fluctuations of the terrain on both sides, realize the adaptive adjustment of the header to uneven ground, and has good stability and anti-interference. Field test verification: compared with the manual mode, the coefficient of variation (CV%) of stubble height in the automatic mode is 40%similar to 60% lower than that in the manual mode, and the CV% remains <= 11.76 when the vehicle speed rises to 2m/s, significantly improving the adaptability to complex terrain.

Buckwheat is a highly nutritious coarse grain crop, yet its industrial processing has long faced two major challenges: the low whole-kernel rate of domestic dehullers and the poor local adaptability of imported equipment. To address these problems, a novel grinding disc-type dehulling machine was developed, featuring upper and lower discs with alternating deep–shallow composite textures to reduce kernel breakage and improve whole kernel rate. A 0–10 mm adjustable gap mechanism was incorporated to suit different buckwheat varieties and particle sizes, enhancing dehulling efficiency. Buckwheat grains were classified into four size ranges: 4.0–4.5 mm, 4.5–5.0 mm, 5.0–5.3 mm, and 5.3–5.7 mm. For all sizes, the optimal rotational speed was 12 r/min, with corresponding optimal gaps of 2.53 mm, 2.80 mm, 3.20 mm, and 3.40 mm, respectively. The whole-kernel rates under these conditions were 32.9%, 37.5%, 45.6%, and 55.1%, respectively, all above 30%, showing substantial improvement. For the 4.5–5.0 mm fraction, orthogonal tests revealed that a small gap (2.859 mm) achieved a dehulling rate of 89.9% and a whole-kernel rate of 38.03%, making it suitable for mass production. A larger gap (3.288 mm) combined with secondary dehulling increased the cumulative whole kernel rate to 50.26%, which is advantageous for producing high value-added products. The novel grinding disc structure balanced frictional and compressive forces on kernels, while the adjustable gap design improved adaptability. Combined with size classification and parameter optimization, this approach provides precise processing schemes for various buckwheat varieties and offers both theoretical and practical value for industrial application.

In response to Chinese liquor brewing industry's need for precise control of sorghum tannin content and the issues of low efficiency and high cost associated with traditional detection methods, this study proposes a non-destructive detection method for sorghum tannin content based on the fusion of dual hyperspectral sensor features. Based on 240 representative sorghum samples covering different varieties and production regions, visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data were sequentially collected, and the tannin content was determined using standard chemical methods as reference values. Using the competitive adaptive reweighted sampling (CARS) method, characteristic wavelength bands were extracted and fused feature subsets were constructed. Combined with partial least squares (PLS), support vector machine (SVM), and convolutional neural network (CNN) algorithms, the performance of models built from both full-data concatenation and feature fusion of VNIR and SWIR data was systematically compared. The results demonstrated that the feature-based models exhibited superior performance to the full-spectrum models, while the model incorporating dual-sensor feature fusion achieved the best overall results. And the Fused-Feature-CNN model achieved the optimal prediction performance, with values of 0.8298 for RP2, 0.2894 for RMSEP, and 2.4239 for RPDP. This study confirms that the integration of multi-sensor feature fusion with deep learning strategies can provide an effective technical pathway for the rapid, non-destructive detection of sorghum tannin content and the development of online sorting equipment.

For the automatic control of soybean harvester header height, this study uses Area array LiDAR for header height detection. An improved quartile range algorithm is used to dynamically remove outliers under crop residue interference. Linear, quadratic, and cubic nonlinear terrain fitting models are established based on the surface undulation characteristics of soybean fields. The Huber loss function is introduced to enhance the robustness of parameter estimation. The balance between model complexity and fitting goodness is quantified using Bayesian information criterion (BIC), and the model intercept term with the smallest BIC value is selected as the terrain reference height. Aiming at the hysteresis characteristics of valve controlled asymmetric hydraulic cylinders, a telescopic dual-mode transfer function model is established, and a Bang-bang switch lead compensation strategy with position threshold is proposed. By predicting the trend of terrain changes, the electromagnetic directional valve is triggered in advance when the height error of the header exceeds the set threshold, effectively reducing the system response delay. Field comparative experiments have shown that at a working speed of 1m/s, the automatic control mode significantly improves the uniformity of cutting compared to the manual mode. When the cutting threshold is set to 20, 25, and 30 mm, the coefficient of variation of cutting height is reduced by 2.13%, 1.71%, and 0.55%, respectively. Moreover, the automatic mode maintains a gentle distribution characteristic within the threshold range of 15-35 mm, verifying the strong robustness and control accuracy advantages of the designed system in complex farmland environments.

To address the demand for precise sorghum tannin control in liquor brewing, and to overcome the inefficiency and high cost of traditional methods, this study developed a non-destructive approach by fusing features from dual hyperspectral sensors. Based on 240 representative sorghum samples covering different varieties and production regions, visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data were sequentially collected, and the tannin content was determined using standard chemical methods as reference values. Using the competitive adaptive reweighted sampling (CARS) method, characteristic wavelength bands were extracted and fused feature subsets were constructed. Combined with partial least squares (PLS), support vector machine (SVM), and convolutional neural network (CNN) algorithms, the performance of models built from both full-data concatenation and feature fusion of VNIR and SWIR data was systematically compared. The results demonstrated that the feature-based models exhibited superior performance to the full-spectrum models, while the model incorporating dual-sensor feature fusion achieved the best overall results. The fused-feature-CNN model achieved the optimal prediction performance, with values of 0.83 for coefficient of determination for the prediction set (RP2), 0.29 for root mean squared error for the prediction set (RMSEP), and 2.42 for residual predictive deviation for the prediction set (RPDP). This study confirms that the integration of multi-sensor feature fusion with deep learning strategies can provide an effective technical pathway for the rapid, non-destructive detection of sorghum tannin content and the development of online sorting equipment.

Feed rate is a critical operating parameter in combine harvesters, as it directly influences working efficiency and harvesting quality. To enable real-time and accurate feed rate monitoring, this study proposes a wireless monitoring approach based on pressure measurements at the bottom plate of the chain-rake conveyor inlet. A coupling model relating feed rate, chain-rake speed, and bottom plate pressure was established through theoretical analysis. To enhance robustness against signal fluctuations, the trimean was selected as the feature metric. A monitoring system incorporating LoRa wireless transmission was developed to achieve data acquisition, transmission, and visualization. Bench test results showed that the established model achieved a goodness-of-fit R² of 0.9963, and the predictive relative error was below 5% under most working conditions, demonstrating that the system provides high accuracy and stability in complex operating environments. The proposed method offers an effective technical solution for feed rate monitoring in combine harvesters.

Nondestructive, rapid, and accurate detection of nutritional compositions in sorghum is crucial for agricultural and food industries. In our study, the crude protein, tannin, and crude fat contents of sorghum variety samples were taken as the research object. The visible near-infrared (VIS-NIR) hyperspectral of sorghum were measured by the indoor mobile scanning platform. The nutritional components were determined using chemical methods to analyze the differences in nutritional composition among different varieties. After preprocessing the original spectral, the competitive adaptive reweighted sampling (CARS) and bootstrapping soft shrinkage (BOSS) algorithms were used to coarsely extract the key variables. Subsequently, the iteratively retains informative variables (IRIV) was employed to assess the importance of these key variables, resulting in explanatory wavelength sets for crude protein, tannin, and crude fat. Finally, the partial least squares (PLS), back propagation (BP) and extreme learning machine (ELM) were utilized to establish detection models. The results indicated that the optimal wavelength variable sets for crude protein, tannin, and crude fat contained 41, 38, and 22 wavelength variables, respectively. The CARS-IRIV-PLS, BOSS-IRIV-PLS and BOSS-IRIV-ELM were suitable for detecting crude protein, tannin and crude fat, respectively. Meanwhile, the Rp2, RMSEp and RPDp values of the model were 0.69, 0.80% and 1.80, 0.88, 0.22% and 2.84, 0.61, 0.32% and 1.61, respectively. These detection models can be used for the effective estimation of the nutritional compositions in sorghum with VIS-NIR spectral data, and can provide an important basis for the application of food nutrition assessment.

In order to enable oat ears to be quickly and accurately identified in the natural environment, this paper proposes an oat ears detection and counting model based on an improved Faster R-CNN. In the backbone network, the commonly used single convolutional neural network is replaced by a parallel convolutional neural network to realize the feature extraction of oat ears, and a feature pyramid network (FPN) is incorporated to improve the small target-missed detection problem and the multi-scale problem of oat ears. Then, the anchor box configuration is optimized according to the size and distribution of the labeled boxes in the dataset, which improves the efficiency of the model to detect oat ears. Finally, progressive non-maximum suppression (Progressive-NMS) was used to replace non-maximum suppression (NMS) to optimize the screening process of prediction boxes. According to the data from different experiments designed, the optimized model can effectively detect oat ears in the natural environment and complete the counting of oat ears per unit area. Compared with the traditional Faster R-CNN detection model, the mean average precision (mAP) of the improved model is increased by 13.01%, which could provide reference for oat yield prediction and intelligent operation.

Precision agriculture technology has become a crucial means of improving the quality of crop production. As an emerging technology in farmland management, intelligent weeding robots utilize intelligent spraying systems to effectively manage weeds, adjusting the types and dosages of herbicides in a timely manner. The accuracy and real-time performance of weed identification algorithms are the keys to intelligent weeding. This study established a proprietary dataset comprising 6690 images of soybean seedlings and weeds and proposed an improved lightweight algorithm, YOLOv8-ECFS. Based on YOLOv8s, this model introduces the EfficientNet network to improve feature extraction capability and accelerate the inference speed, replacing the CIoU loss with Focal_SIoU to optimize the regression accuracy of the bounding boxes. Furthermore, the coordinate attention module is introduced into the neck to enable the model to precisely capture textural and color differences between various weeds and soybean crops, thereby ensuring precise identification of multiple weed species. The results demonstrate that YOLOv8-ECFS achieves precision, mAP, and F1 values of 92.2%, 95.0%, and 90.9%, representing an increase of 2.5%, 1.3%, and 1.6%, respectively, compared to YOLOv8s. Simultaneously, the model's GFLOPs and model size have been reduced by 11.1G and 9.1 MB, respectively, ensuring both recognition accuracy and lightweight performance. The test set results show that YOLOv8-ECFS accurately identifies densely growing and mutually occluding weeds, reducing cases of false positives and missed detections. Compared to other mainstream YOLO algorithms, YOLOv8-ECFS demonstrates the best overall performance, thus providing support for intelligent weeding robots in farmland management and unmanned farms.

To reduce grain loss during pickup and prevent stalk entanglement in buckwheat harvesters, thereby improving the quality of mechanized harvesting, a three-stage pickup conveyor roll with a ground-level rotary knife-type pickup header was designed and tested. This paper, based on the growth characteristics of buckwheat, determined the three-stage pickup conveying process and the overall structure of the ground-level rotary knife-type pickup header. Kinematic analysis and parameter design of the rotary knife-type pickup roll were conducted. Finally, a physical prototype was fabricated, and field performance tests were carried out, using machine forward speed and pickup roll rotational speed as influencing factors and pickup loss rate as an evaluation metric. Results showed that the interaction between pickup roll rotational speed and forward speed had a significant effect on the pickup loss rate, with forward speed having a greater impact than pickup roll rotational speed. Under consideration of their interaction, when the pickup roll rotational speed was within the range of 396–457 r/min, and the forward speed was between 0.9–1.0 m/s, the pickup loss rate was minimized. Based on the regression equation model, the predicted optimal conditions were a forward speed of 1.0 m/s and a roll pickup speed of 396 rpm. Under these conditions, the test results showed a pickup loss rate of 5.235%, indicating good pickup performance. This research provides a reference for the design of pickup devices in grain combine harvesters.

Non-destructive, fast, and accurate prediction of soil organic matter content in farmland is of great significance for soil fertility assessment and rational fertilization. In the process of soil organic matter prediction, it is important to give full play to the advantages of different prediction models and to integrate different prediction models to innovatively construct a combined prediction model of soil organic matter content so as to improve the prediction accuracy and generalization ability of the model. In this study, the soil organic matter content of agricultural soils was taken as the research object, and the visible near-infrared hyperspectral curves of soils were measured by the Starter Kit indoor mobile scanning platform (Headwall Photonics, Bolton, MA, USA), and the original spectral curves were firstly de-noised by Savitzky–Golay (S-G) smoothing. Secondly, the smoothed and denoised spectral data were subjected to a first-order differential transform, and the features were selected based on the first-order differential transformed spectral data using the L1-paradigm algorithm features. Then, secondly, eight algorithms based on the selected feature bands, such as LASSO Regression (LASSO) (Model 1), Multilayer Perceptron (MLP) (Model 2), Random Forest (RF) (Model 3), Gaussian Kernel Regression (GKR) (Model 4), Ridge Regression (Model 5), Long Short-Term Memory (LSTM) (Model 6), Convolutional Neural Networks (CNN) (Model 7), and Support Vector Regression (SVR) (Model 8), were applied to construct a single-prediction model of soil organic matter content. Finally, a superior linear combination-prediction model was proposed by the eight single-prediction models constructed, and the standard deviation-based prediction validity was added to test the model. The results showed the following: (1) the weights of the eight single-prediction models in the combined prediction model were ω1*=0.099, ω2*=0.202, ω3*=0.000, ω4*=0.357, ω5*=0.088, ω6*=0.089, ω7*=0.000, ω8*=0.165, respectively; (2) The average precision E of the predicted values of soil organic matter content constructed based on the eight single-prediction models was 0.856; the average standard deviation σ was 0.181, and the average prediction validity M was 0.702; (3) The accuracy E of the predicted value of soil organic matter content of the combined model was 0.893, which was 4.322% higher than the average accuracy of the single model; the standard deviation of the combined model was 0.129, which was 28.333% lower than the average standard deviation of the single model, and the prediction validity M of the combined model was 0.778, which was 10.826% higher than the average prediction validity of the single model. The combined model can be used for the effective estimation of soil organic matter content in farmland with visible near-infrared spectral data, which can provide a basis and reference for the rapid detection of soil organic matter content in farmland.

Accurately grasping the total nitrogen content of farmland soil is significant for evaluating soil fertility and applying nitrogen fertilizer reasonably. To comprehensively utilize the advantages of each single prediction Model, improve the overall prediction performance, reduce the variance of the model, and improve the robustness, this study takes farmland brown soil as the research object, and based on near-infrared and visible hyperspectral data, puts forward a Combined prediction model based on standard deviation. CPM was used to predict soil total nitrogen content. Savitzky-Golay smoothing and first-order differential transformation are applied to the original hyperspectral data, and a tree model is used for feature band extraction. Using five single prediction models, Decision Tree Regression (DTR) (Model 1), Gaussian Kernel Regression (GKR) (Model 2), Random Forest Regression (RF) (Model 3), LASSO Regression (Model 4), and Multi-Layer Perceptron (MLP) (Model 5), a combination prediction model is established through a linear combination of single prediction models. The results indicate that: (1) The weights of the five single prediction models in the combined prediction model are obtained by generalized reduced gradient optimization algorithm: omega(1)* = 0. 407, omega(2)* = 0. 378, omega(3)* = 0. 215, omega(4)* = 0, omega(5)* =0; (2) For all data, the predictive effectiveness of five single prediction models and combined prediction models for predicting soil total nitrogen content is M, respectively M-1 = 0. 855, M-2 = 0. 856, M-3 = 0. 847, M-4 = 0. 785, M-5 = 0.796, M-CPM = 0. 880, compared to the maximum predictive validity of a single model, the predictive validity of the combination prediction model has increased by 2. 924%; (3) For all data, the prediction accuracy and standard deviation of soil total nitrogen content based on five single prediction models and combined prediction models are E(A(1)) =0. 924, sigma(A(1)) = 0. 075, E(A(2)) = 0. 928, sigma(A(2)) = 0. 077, E(A(3))= 0. 923, sigma(A(3)) = 0. 082, E(A(4))=0. 882, sigma(A(4))=0. 109, E(A(5)) =0. 889, sigma(A(5)) = 0. 104, E(A(CPM)) =0. 937, sigma(A(CPM)) = 0. 066, compared to the maximum prediction accuracy of a single model, the combination prediction model has improved prediction accuracy by 0. 970% and model stability by 12. 000%, making it an optimal combination prediction model. The combined prediction model can effectively estimate the total nitrogen content of farmland brown soil based on visible-near-infrared spectral data and can provide a basis and reference for the rapid monitoring of the total nitrogen content of farmland soil.

Buckwheat grains will suffer varying degrees of damage during the threshing process. Damaged grains are prone to changes in nutritional quality during storage. To explore the relationship between the threshing damage mechanism and nutritional quality, the mechanical properties of buckwheat with different moisture contents were determined through compression and friction tests, obtaining some conventional mechanical property indicators. On this basis, a 3D collision model of buckwheat–nail tooth was established, and the dynamic process of collision was simulated with LS-DYNA. During collision, the changes in energy, von Mises stress, and critical damage velocity of grains with different moisture contents were analyzed. After the threshing test, the grains were observed and classified according to the degree of damage. The differences in nutritional components of different types of grains were analyzed through physicochemical experiments. The experimental results showed that the failure force, elastic modulus, and ultimate strength of buckwheat grains were negatively correlated with moisture content, while the deformation and friction coefficient were positively correlated with moisture content. During collision, the von Mises stress of the grains showed a pattern of increase and then decrease. The maximum stress value occurred at the contact area center, spreading along the periphery and gradually decreasing. The maximum von Mises stress decreased with increasing moisture content and increased with increasing collision velocity. The critical damage velocities of grains at moisture contents of 11.98%, 15.77%, 18.04%, 20.82%, and 25.22% were 13.07, 11.72, 10.94, 10.55, and 10.15 m/s, respectively. After threshing, grains were divided into three types: undamaged, surface cracked, and shell damaged. The nutritional quality of the last two damaged grains decreased during the storage process. These results are of great significance for optimizing buckwheat threshing parameters, reducing buckwheat damage, and improving the economic performance of the buckwheat industry.

Plant counting plays an important role in evaluating planter effectiveness, assessing seed quality, devising agricultural management plans, and estimating crop yields. Given its significance and the ease of acquiring agricultural images, the development of an end-to-end image-based plant counting model applicable across diverse agricultural settings is crucial. The proposed TasselNetV2++, an improved version of TasselNetV2+ for plant counting, introduces notable enhancements to its encoder and counter while maintaining the existing normalizer. In the encoder, we designed a dual-branch architecture, with one branch being a customized YOLOv5s backbone and the other branch being the original encoder equipped with an attention mechanism. It is precisely the branch-level transfer learning, coupled with multilayer fusion, within the dual-branch architecture that significantly enhances the feature extraction capability of the network across a wide range of scenarios. Moreover, the counter has been enhanced with an attention mechanism that recalibrates its focus on crucial spatial locations and channel-wise features following average pooling. Experimental results demonstrate that TasselNetV2++ outperforms its predecessor across multiple counting tasks. Compared to TasselNetV2+, TasselNetV2++ achieves a substantial reduction in relative root mean squared error (rRMSE). Specifically, it brings a 33.3% relative decrease of rRMSE on the soybean seedlings counting dataset, 8.4% on the wheat ears detection dataset, 28.6% on the maize tassels counting dataset, and 18.0% on the sorghum heads counting dataset. Notably, ablation experiment demonstrates the indispensability of the branch-level transfer learning in achieving precise plant counting. Branch-level transfer learning achieves a notable relative decrease in rRMSE of 31.4% for soybean seedlings, 7.9% for wheat tassels, 36.5% for maize tassels, and 2.0% for sorghum heads. The proposed TasselNetV2++ attains remarkable advancements and introduces a straightforward yet highly effective branch-level transfer learning strategy.

For buckwheat, the optimal harvest period is difficult to determine—too early or too late a harvest affects the nutritional quality of buckwheat. In this paper, physical and chemical tests are combined with a method using near-infrared spectroscopy nondestructive testing technology to study buckwheat harvest and determine the optimal harvest period. Physical and chemical tests to determine the growth cycle were performed at 83 days, 90 days, 93 days, 96 days, 99 days, and 102 days, in which the buckwheat grain starch, fat, protein, total flavonoid, and total phenol contents were assessed. Spectral images of buckwheat in six different harvest periods were collected using a near-infrared spectral imaging system. Four preprocessing methods (SNV, S-G, DWT, and the normaliz function) and three dimensionality reduction algorithms (IVSO, VCPA, VISSA) were used to process the raw buckwheat spectral data, and the full and eigen spectra were established as a random forest (RF). Random forest (RF) and Least Squares Support Vector Machine (LS-SVM) classification models were used to determine the full and eigen spectra, respectively, and the optimal model for the buckwheat single harvest period was determined and validated. Through physical and chemical tests, it was concluded that the 90-day harvest buckwheat grain protein, fat, and starch contents were the highest, and that the total flavonoid and total phenolic contents were also high. The SNV preprocessing method was the most effective, and the feature bands extracted using the IVSO algorithm were more representative. The IVSO-RF model was the best discriminative model for the classification of buckwheat in different harvest periods, with the correct rates of the training and prediction sets reaching 100% and 96.67%, respectively. When applying the IVSO-RF model to the buckwheat single harvest period to verify the classification, the correct rate of the training set for each harvest period reached 96%, and that of the prediction set reached 100%. Near-infrared spectroscopy combined with the IVSO-RF modeling method for buckwheat harvest period detection is a rapid, nondestructive classification method. When this was combined with physical and chemical analyses, it was determined that a growth cycle of 90 days is the best harvest period for buckwheat. The results of this study can not only improve the quality of buckwheat crops but also be applied to other crops to determine their optimal harvest period.

During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is an urgent need to enhance the automation level. Conventional methods frequently employ single-point measurements and lack extensive area coverage, which means their results do not fully represent the terrain’s variations in the area and are prone to local anomalies. Given the inherently undulating terrain of farmland during harvesting, a control strategy that does not adjust for minor undulations but only for significant ones proves to be more rational. To this end, a sine wave superposition model was established to simulate three-dimensional ground elevation changes, and an area array LiDAR was used to collect 8 × 8 data for the header height. The effects of mounds and stubble on the measurement results were analyzed, and a dynamic process simulation model for the solenoid valve core was developed to analyze the on/off delay characteristics of a three-position four-way electromagnetic directional valve. Moreover, a physical model of the hydraulic system was constructed based on the Simscape module in Simulink, and the Bang Bang switch predictive control system based on position threshold was introduced to achieve early switching of the electromagnetic directional valve circuit. In addition, an automatic control system for cutting platform height was designed based on an STM32 microcontroller. The control system was tested on the hydraulic automatic control test rig developed by Shanxi Agricultural University. The simulation and experimental results demonstrated that the control system and strategy were robust to output disturbances, effectively enhancing the intelligence and environmental adaptability of agricultural machinery operations.