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.

The mechanized sowing operation of corn breeding can improve test accuracy and efficiency and speed up the test process. In view of the problems of low flexibility, high price, and poor sowing quality of the current seeder in corn breeding experiments, the critical components of a hand-pushing corn plot precision planter were optimized. Through the analysis of the relative motion between corn seed and the hole seed-metering wheel, the hole diameter range and hole depth range of the hole seed-metering wheel were determined as 13~19 mm and 6~12 mm, respectively. The chamber structure was optimized to improve the seed filling performance under the condition of a small number of seeds. The angle range between the connecting plate and the horizontal line (ACP) and the angle range between the back plate and the horizontal line (ABP) were 35~55° and 40~70°, respectively. EDEM was used to simulate the seeding circumstances of hole seed-metering wheel with different holes sizes. The optimal combination of hole diameter and depth under different seeds’ sizes and the appropriate angle combination of ACP and ABP were obtained through the orthogonal test. The results of the simulation experiment showed that the optimal hole diameter–depth combinations of the small, medium, and large corn seeds were 13–8, 15–10, and 17–8 mm, respectively. The angle ACP and the angle ABP were, respectively, 45°and 70°. The soil bin test was carried out to further verify the actual seeding effect and the influence of the rotating speed of hole seed-metering wheel on seeding. The results showed that when the rotating speed of hole seed-metering wheel was less than 16.0 r/min, the qualified seeding rates and the double seeds rates of the small, medium, and large corn seeds were higher than 90% and 85%, respectively. The missing seeding rates were 0%. The variation coefficients of plant spacing were less than 5%, indicating good stability of plant spacing, which met the design requirements. The study results can provide a theoretical reference for the optimization design of maize breeding equipment in a plot.

Soil compaction is a common problem facing conservation fields that restricts crop root growth and causes yield decrease. Subsoil techniques have been developed to break up the compaction layer. However, subsoil implement requires large draft power that hampered the development of subsoil techniques for most of developing countries due to lack of large scale tractors. Aiming to optimize the penetration resistance of the subsoiler and create a good working environment for the operators, a staggered vibrating subsoiler was developed. A new staggered vibrating mechanism was designed to generate the staggered vibration of the shanks meanwhile the V-shape shanks arrangement was adopted to keep relative balance for the subsoiler. In order to obtain optimum working parameters of the vibration frequency and forward speed, the trajectory of shanks was simulated by using the MATLAB software. The forward speed of 2-3 km/h with vibration frequency of 12 Hz was recommended to acquire an effective decrease in draft force. Field performance of this subsoiler was evaluated in terms of the draft force, power requirement and tractor wheel slippage. By comparing the two operation modes, staggered vibrating (SV) and rigid (NV) of shanks, the decrease ratios of draft force for SV were determined by 16.97%, 12.12% and 9.02% at forward speeds of 2.2 km/h, 2.6 km/h and 3.1 km/h, respectively. This is better than the research for the 1SZ-460 vibratory subsoiler that was decreased by 9.09% in draft force. The power requirement for SV was not significantly greater than that for NV. The obviously decreased wheel slippage was observed for SV by decrease of 12.47%, 17.96% and 21.79% at forward speeds of 2.2 km/h, 2.6 km/h and 3.1 km/h, respectively. In conclusion, the staggered vibrating subsoiler presents preferable working performance and is recommended to be applied in subsoil tillage process for developing countries. Keywords: vibrating subsoiler, staggered vibrating mechanism, V-shape arrangement, field experiment DOI: 10.25165/j.ijabe.20191201.4297 Citation: Wang Y X, Osman A N, Zhang D X, Yang L, Cui T, Zhong X J. Optimized design and field experiment of a staggered vibrating subsoiler for conservation tillage. Int J Agric & Biol Eng, 2019; 12(1): 59–65.

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.