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Corn silage is the main feed in the diet of dairy cows and other ruminant livestock. Silage corn feed is very susceptible to spoilage and corruption due to the influence of aerobic secondary fermentation during the silage process. At present, silage quality testing of corn feed mainly relies on the combination of sensory evaluation and laboratory measurement. The sensory review method is difficult to achieve precision and objectivity, while the laboratory determination method has problems such as cumbersome testing procedures, time-consuming, high cost, and damage to samples. In this study, the external sensory quality grading model for different qualities of silage corn feed was established using hyperspectral data. To explore the feasibility of using hyperspectral data for external sensory quality grading of corn silage, a hyperspectral system was used to collect spectral data of 200 corn silage samples in the 380–1004 nm band, and the samples were classified into four grades: excellent, fair, medium, and spoiled according to the German Agricultural Association (DLG) standard for sensory evaluation of silage samples. Three algorithms were used to preprocess the fodder hyperspectral data, including multiplicative scatter correction (MSC), standard normal variate (SNV), and S–G convolutional smoothing. To reduce the redundancy of the spectral data, variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS) were used for feature wavelength selection, and linear discriminant analysis (LDA) algorithm was used for data dimensionality reduction, constructing random forest classification (RFC), convolutional neural networks (CNN) and support vector machines (SVM) models. The best classification model was derived based on the comparison of the model results. The results show that SNV-LDA-SVM is the optimal algorithm combination, where the accuracy of the calibration set is 99.375% and the accuracy of the prediction set is 100%. In summary, combined with hyperspectral technology, the constructed model can realize the accurate discrimination of the external sensory quality of silage corn feed, which provides a reliable and effective new non-destructive testing method for silage corn feed quality detection.

Lactic acid content is a crucial indicator for evaluating maize silage quality, and its accurate detection is essential for ensuring product quality. In this study, a quantitative prediction model for the change of lactic acid content during the secondary fermentation of maize silage was constructed based on a colorimetric sensor array (CSA) combined with hyperspectral imaging. Volatile odor information from maize silage samples with different days of aerobic exposure was obtained using CSA and recorded by a hyperspectral imaging (HSI) system. Subsequently, the acquired spectral data were subjected to preprocessing through five distinct methods before being modeled using partial least squares regression (PLSR). The coronavirus herd immunity optimizer (CHIO) algorithm was introduced to screen three color-sensitive dyes that are more sensitive to changes in lactic acid content of maize silage. To minimize model redundancy, three algorithms, such as competitive adaptive reweighted sampling (CARS), were used to extract the characteristic wavelengths of the three dyes, and the combination of the characteristic wavelengths obtained by each algorithm was used as an input variable to build an analytical model for quantitative prediction of the lactic acid content by support vector regression (SVR). Moreover, two optimization algorithms, namely grid search (GS) and crested porcupine optimizer (CPO), were compared to determine their effectiveness in optimizing the parameters of the SVR model. The results showed that the prediction accuracy of the model can be significantly improved by choosing appropriate pretreatment methods for different color-sensitive dyes. The CARS-CPO-SVR model had better prediction, with a prediction set determination coefficient (RP2), root mean square error of prediction (RMSEP), and a ratio of performance to deviation (RPD) of 0.9617, 2.0057, and 5.1997, respectively. These comprehensive findings confirm the viability of integrating CSA with hyperspectral imaging to accurately quantify the lactic acid content in silage, providing a scientific and novel method for maize silage quality testing.

To improve the operational efficiency of a hammer mill and delve into a high-efficiency, energy-saving grinding mechanism, the crucial parameters influencing the grinding of corn straw were identified as the spindle speed, hammer–sieve gap, and sieve pore diameter. According to the force analysis and kinematics analysis, the key factors affecting corn straw grinding were the spindle speed, the hammer–sieve gap, and the sieve pore diameter. The grinding process of corn straw was studied using computational fluid dynamics (CFDs) and the discrete element method (DEM) gas–solid coupling numerical simulation and experiment. The numerical simulation results showed that with the growth of time, the higher the spindle speed, the faster the bonds broke in each part, and the higher the grinding efficiency. When the energy loss of the hammer component was in the range of 985.6~1312.2 J, and the total collision force of the corn straw was greater than 47,032.5 N, the straw grinding effect was better, and the per kW·h yield was higher. The experimental results showed that the optimum combination of operating parameters was a spindle speed of 2625 r/min, a hammer-screen gap of 14 mm, and a sieve pore diameter of 8 mm. Finally, the CFD–DEM gas–solid coupling numerical simulation validation tests were performed based on the optimal combination of the operating parameters. The results showed that the energy loss of the hammer component was 1189.5 J, and the total collision force of the corn straw was 49,523.5 N, both of which were within the range of better results in terms of numerical simulation. Thus, the CFD–DEM gas–solid coupling numerical simulation could accurately predict the corn straw grinding process. This study provides a theoretical basis for improving a hammer mill’s key components and grinding performance. Meanwhile, the proposed gas–solid two-phase flow method provided theoretical references for other research in agricultural machinery.

ABSTRACT To improve the working performance of hammer mill, cutting-edge hammer and oblique hammer were designed in this study. The advantages of new hammers were theoretically analyzed. The grinding process of corn grain with different hammers was studied by discrete element method (DEM) and experiments. Discrete element simulation results showed that under same rotor speed conditions, the cutting-edge hammer had highest bond-breaking efficiency in corn grain model. The oblique hammer could reduce the incident angle of corn grain and improve sieving efficiency. The motion trajectory of corn grain in grinding chamber was relatively dispersed and similar when using common hammer and cutting-edge hammer, and the motion trajectory was more concentrated when oblique hammer was used. The experimental results showed that both cutting-edge hammer and oblique hammer could improve the working performance of hammer mill. The productivity of hammer mill could be improved by using cutting-edge hammer, electricity consumption per ton and temperature rise of feed could be reduced by using oblique hammer, and the experimental results were consistent with simulation results and theoretical analysis results. The research results can provide references for the design of new hammer and the grinding process simulation of other agricultural materials.

ABSTRACT The harvesting of straw by the flail knife type straw cutting device will cause loose contacts between straw roots and soil, affecting the straw feed's impurity content. In this study, a theoretical analysis of the straw cutting process was conducted to explore the factors influencing the root-soil disturbance. A pulling force test device was designed to test the pulling force of the corn stalk. The response surface method was used to study the effects of various factors on the straw pulling force under different conditions of draft angles (20°, 30°, 40°), soil moisture content (15.23%, 17.62%, 20.47%), and different straw root diameters. The test results showed that the straw pulling force was directly proportional to the straw root diameter and inversely proportional to the soil moisture content. The straw pulling force decreased first and then increased with the increase of the draft angle. According to the established second-order regression model, when the draft angle was 28.5°, the soil moisture content was 20.47%, the root diameter was 22 mm, the minimum pulling force of straw was 189.635N. The test results can provide a reference for the design of straw feed harvester.

pH value is a crucial indicator for evaluating silage quality. In this study, taking maize silage as the research object, a quantitative prediction model of pH value change during the secondary fermentation of maize silage was constructed based on computer vision. Firstly, maize silage samples were collected for image acquisition and pH value determination during intermittent and always-aerobic exposure. Secondly, after preprocessing the acquired image with the region of interest (ROI) interception, smoothing, and sharpening, the color and texture features were extracted. In addition, Pearson correlation analysis and RF importance ranking were used to choose useful feature variables. Finally, based on all feature variables and useful feature variables, four regression models were constructed and compared using random forest regression (RFR) and support vector regression (SVR): RFR model 1, RFR model 2, SVR model 1, and SVR model 2. The results showed that—compared with texture features—the correlation between color features and pH value was higher, which could better reflect the dynamic changes in pH value. All four models were highly predictive. The RFR model represented the quantitative analysis relationship between image information and pH value better than the SVR model. RFR model 2 was efficient and accurate, and was the best model for pH prediction, with Rc2, Rp2, RMSEC, RMSEP, and RPD of 0.9891, 0.9425, 0.1758, 0.3651, and 4.2367, respectively. Overall, this study proved the feasibility of using computer vision technology to quantitatively predict pH value during the secondary fermentation of maize silage and provided new insights for monitoring the quality of maize silage.