Aiming at solving the problem of a wide variety of crop planting and addressing the concept of precision agriculture, a pneumatic universal seed-metering device suitable for corn and soybean was designed. According to the physical size of the above two crops crop planting, a seeding plate, a hole, and a guide tube were designed. The pressure distribution inside the seeding plate was studied, when the pressure, the diameter of the hole, and the rotation speed of the metering plate changed. Through the coupling simulation method of DEM and CFD, the effects of the air suction hole diameter, the air pressure intensity, and the seeding plate speed on the seeding performance were explored. The results showed that when the air suction hole diameter was 5.9 mm, the air pressure intensity was 3.5 kPa, and the seeding plate speed was 23.8 r/min, and the performance of corn seeding was the best, among which the seeding qualification index was 95.35%, the replay index was 1.45%, and the missed seeding index was 3.23%. When the air suction hole diameter was 6.1 mm, the air pressure intensity was 3.5 kPa, and the rotation speed of the seed plate was 24 r/min, the performance of soybean sowing was the best, in which the sowing qualification index was 95.76%, the reseeding index was 3.47%, and the missed sowing index was 0.77%. The bench verification test and the comparative test were carried out. The results showed that the seed-metering device had good seeding performance and could be applied to the general seeding operation of corn and soybean.

Soil nutrients are essential for plant growth, and it is crucial to accurately detect their levels. However, current methods for detecting soil nutrients still have low accuracy and high costs. In this study, we propose a multi-source fusion feature approach that combines multispectral image technology with electronic nose gas response technology to achieve rapid, high-precision, and cost-effective detection of soil nutrients, including soil organic matter (SOM), total nitrogen (TN), available potassium (AK), and available phosphorus (AP). To begin, a multispectral camera was used to collect spectral images and extract seven index features. Additionally, an electronic nose was used to collect soil pyrolysis gases to obtain the response curve and extract seven response features. These two sets of features were effectively fused to form a 106 × 98 fusion feature space. Secondly, principal component analysis (PCA) and Pearson correlation coefficient (PCC) algorithms were used to optimize and reduce the dimensionality of the fused feature space. Finally, a predictive model of the relationship between the feature space and the nutrient content of the soil samples was established using the random forest (RF) and partial least squares regression (PLSR) algorithms. The predictive performance of the model was used to evaluate the accuracy of soil nutrient detection. The results showed that the PLSR modeling of the optimized feature space of PCA achieved high accuracy in predicting the levels of main soil nutrients, with R² values for SOM, TN, AK, and AP of 0.96, 0.95, 0.84, and 0.73, and the RMSE values of 0.56, 0.07, 3.21, and 3.70, respectively. Compared to using only electronic nose gas response technology, the soil nutrient detection with multi-source data features method in this study not only improved the accuracy for SOM and TN but also for metal elements AK and AP in soil. The spectral index features proposed in this study were able to compensate for the limitations of the electronic nose response features, and modeling with the fusion feature space resulted in an accurate prediction of SOM, TN, AK, and AP levels.

Design and experiment of high-speed straw cleaning unit with double air springs

Farmland soil respiration (Rs) significantly impacts the global carbon (C) cycle. Although nitrogen (N) can promote crop growth and increase yields, its relationship with Rs and its constituents, including autotrophic respiration (Ra) and heterotrophic respiration (Rh), remains unclear. Therefore, a field study was carried out in a cabbage (Brassica pekinensis Rupr) system to probe the impact of N addition on Rs, Ra, and Rh. Five levels of N addition, including 0 kg N hm⁻²·yr⁻¹ (N0), 50 kg N hm⁻²·yr⁻¹ (N50), 100 kg N hm⁻²·yr⁻¹ (N100), 150 kg N hm⁻²·yr⁻¹ (N150), and 200 kg N hm⁻²·yr⁻¹ (N200), started in March 2022. The Rs (Ra and Rh) and soil samples were measured and collected twice a month. The findings revealed the following: (1) N fertilizer enhanced Ra while reducing Rs and Rh; (2) soil temperature (ST), belowground net primary productivity (BNPP), soil inorganic N (SIN), and soil total C/total N (C/N) were the significant elements influencing Ra, and microbial biomass carbon (MBC), SIN, and microbial diversity (MD) were the primary factors influencing Rh; (3) partial least squares-path models (PLS-PM) showed that ST and SIN directly impacted Rh, while ST and BNPP tangentially influenced Ra; (4) 150 kg N hm⁻²·yr⁻¹ was the ideal N addition rate for the cabbage in the region. In summary, the reactions of Ra and Rh to N fertilizer in the Northeast Plains are distinct. To comprehend the underlying processes of Rs, Ra, and Rh, further long-term trials involving various amounts of N addition are required, particularly concerning worsening N deposition.

Soil classification stands as a pivotal aspect in the domain of agricultural practices and environmental research, wielding substantial influence over decisions related to real-time soil management and precision agriculture. Nevertheless, traditional methods of assessing soil conditions, primarily grounded in labor-intensive chemical analyses, confront formidable challenges marked by substantial resource demands and spatial coverage limitations. This study introduced a machine olfaction methodology crafted to emulate the capabilities of the human olfactory system, providing a cost-effective alternative. In the initial phase, volatile gases produced during soil pyrolysis were propelled into a sensor array comprising 10 distinct gas sensors to monitor changes in gas concentration. Following the transmission of response data, nine eigenvalues were derived from the response curve of each sensor. Given the disparate sample counts for the two distinct classification criteria, this computational procedure yields two distinct eigenspaces, characterized by dimensions of 112 or 114 soil samples, each multiplied by 10 sensors and nine eigenvalues. The determination of the optimal feature space was guided by the “overall feature information” derived from mutual information. Ultimately, the inclusion of random forest (RF), multi-layer perceptron (MLP), and multi-layer perceptron combined with random forest (MLP-RF) models was employed to classify soils under four treatments (tillage and straw management) and three fertility grades. The assessment of model performance involved metrics such as overall accuracy (OA) and the Kappa coefficient. The findings revealed that the optimal classification model, MLP-RF, achieved impeccable performance with an OA of 100.00% in classifying soils under both criteria, which showed almost perfect agreement with the actual results. The approach proposed in this study provided near-real-time data on the condition of the soil and opened up new possibilities for advancing precision agriculture management.

Key soil properties play pivotal roles in shaping crop growth and yield outcomes. Accurate point prediction and interval prediction of soil properties serve as crucial references for making informed decisions regarding fertilizer applications. Traditional soil testing methods often entail laborious and resource-intensive chemical analyses. To address this challenge, this study introduced a novel approach leveraging spectral data fusion techniques to forecast key soil properties. The initial datasets were derived from UV–visible–near-infrared (UV-Vis-NIR) spectral data and mid-infrared (MIR) spectral data, which underwent preprocessing stages involving smoothing denoising and fractional-order derivative[s] (FOD) transform techniques. After extracting the characteristic bands from both types of spectral data, three fusion strategies were developed, which were further enhanced using machine learning techniques. Among these strategies, the outer-product analysis fusion algorithm proved particularly effective in improving prediction accuracy. For point predictions, metrics such as the coefficient of determination (R²) and error metrics demonstrated significant enhancements compared to predictions based solely on single-source spectral data. Specifically, R² values increased by 0.06 to 0.41, underscoring the efficacy of the fusion approach combined with partial least squares regression (PLSR). In addition, based on the coverage width criterion to establish reliable prediction intervals for key soil properties, including soil organic matter (SOM), total nitrogen (TN), hydrolyzed nitrogen (HN), and available potassium (AK). These intervals were developed within the framework of the kernel density estimation (KDE) interval prediction model, which facilitates the quantification of uncertainty in property estimates. For available phosphorus (AP), a preliminary assessment of its concentration was also provided. By integrating advanced spectral data fusion with machine learning, this study paves the way for more informed agricultural decision making and sustainable soil management strategies.