Agriculture, Volume 14, Issue 5 (May 2024) – 118 articles

This paper takes the frame as the research object and explores the vibration characteristics of the frame to address the vibration problem of a 1-MSD straw-crushing and residual film recycling machine in the field operation process, and an accurate identification of the modal parameters of the frame is carried out to solve the resonance problem of the machine, which can achieve cost reduction and increase income to a certain extent. The first six natural frequencies of the frame are extracted by finite element modal identification and modal tests, respectively. The rationality of the modal test results is verified using the comprehensive modal and frequency response confidences. The maximum frequency error of modal frequency results of the two methods is only 6.61%, which provides a theoretical basis for the optimal design of the frame. In order to further analyze the resonance problem of the machine, the external excitation frequency of the machine during normal operation in the field is solved and compared with the first six natural frequencies of the frame. The results show that the first natural frequency of the frame (18.89 Hz) is close to the external excitation generated by the stripping roller (16.67 Hz). The first natural frequency and the volume of the frame are set as the optimization objectives, and the optimal optimization scheme is obtained by using the Optistruct solver, sensitivity method, and grey correlation method. The results indicate the first-order natural frequency of the optimized frame is 21.89 Hz, an increase of 15.882%, which is much higher than the excitation frequency of 16.67 Hz, and resonance can be avoided. The corresponding frame volume is 9.975 × 10⁷ mm³, and the volume reduction is 3.46%; the optimized frame has good dynamic performance, which avoids the resonance of the machine and conforms to the lightweight design criteria of agricultural machinery structures. The research results can provide some theoretical reference for this kind of machine in solving the resonance problem and carrying out related vibration characteristics research. Full article

Both arbuscular mycorrhizal (AM) fungi and root hairs are crucial in facilitating plant uptake of phosphorus (P), while it is unclear whether and how they respond to varying P supplies. In order to explore how AM fungal colonization and root hair development are affected by substrate P supply, trifoliate orange (Poncirus trifoliata) seedlings were inoculated with AM fungus Rhizophagus intraradices and grown under low, moderate, and high P conditions; then, root hair morphological features and AM fungal colonization were measured. Following 120 days of AM fungal inoculation, root hair density, root hair length, AM fungal colonization rate, arbuscule colonization rate, and AM fungal colonization frequency all increased significantly under P-deficient conditions but decreased under high P conditions. Moreover, the colonization of AM fungi had a major impact on root hair formation by altering the expression of related genes and the growth of epidermal cells. The effect of AM fungi was dependent on P supply levels, as evidenced by the fact that root hair density and length increased at high P levels but decreased at low P levels. As a result, root hairs may serve as a preferential site for AM fungal colonization, and their morphology could influence the early stage of AM symbiosis establishment. Full article

Whether the stems and leaves of leaf-vegetable sweet potatoes can be listed ahead of schedule is related to the improvement in economic benefits for farmers, and the key to all of this is to implement the safe overwintering of potato seedlings under the premise of saving production costs. Only in this way can we truly seize the “market opportunity” and achieve the goals of cost saving and increasing economic benefit. In this study, the main leaf-vegetable sweet potato variety Fucai 18 was used as the material, and the L9(3⁴) orthogonal experiment was carried out in a simple solar greenhouse environment for two consecutive years from 2021 to 2022 and from 2022 to 2023, respectively. The effects of nine different combinations of factors on the above-ground and underground agronomic traits of overwintering sweet potato seedlings were studied under the conditions of four factors and three levels: planting density (a); different cutting seedlings (b); rooting agent concentration (c); and transplanting time (d). The methods of principal component analysis, membership function method, cluster analysis, grey correlation degree and stepwise regression analysis were used to evaluate the growth of overwintering seedlings, and try to screen out the key indicators that can be used to identify and evaluate the growth of overwintering sweet potato seedlings. Through range analysis, identify the optimal combination of four factors and three levels, and explore the main factors that have a significant impact on the key indicators for evaluating the growth of overwintering potato seedlings. The results indicate the following: (1) The use of simple sunlight greenhouse in Changsha area can achieve the safe overwintering of vegetable sweet potato seedlings. (2) Stem thickness, root length, and root diameter can be used as three key indicators for identifying and evaluating the growth potential of vegetable sweet potato overwintering seedlings. (3) Under four factors and three levels, the best combination was A3B3C1D1 (planting density of 250,000 plants/ha, stem tip core-plucking seedlings, rooting agent concentration of 50 mg/L, the first batch of transplanting time). (4) The transplanting time (D) is the main factor for the two key evaluation indicators of stem diameter and root diameter, while there is no significant difference in the three other factors. (5) Different cutting seedlings (B) are the main influencing factors for the key evaluation index of root length, while the other three factors have the following impact on root length: transplanting time (D) > rooting agent concentration (C) > planting density (A). The results of this study not only contribute to the construction of a safe overwintering cultivation technology system for vegetable sweet potato seedlings, but also provide a certain theoretical basis for the breeding of new cold-leaf-vegetable sweet potato varieties in the future. Full article

Increasing fruit crop production sustainability under climate change, particularly increasing temperatures, is a major challenge in modern agriculture. High temperatures affect apple fruit quality and decrease its color. Herein, we constructed an experimental field under temperature simulation to evaluate climate change mitigation strategies for apples. ‘Hongro’ apples were subjected to three treatments: (1) cultivation inside a vinyl house for heat treatment (heat induction), (2) cultivation under water irrigation (heat reduction), and (3) cultivation under normal atmospheric temperature (control). At harvest, the fruits of the heat treatment group exhibited poor coloration, with a lower gene expression and pigment accumulation than those of the water irrigation and control groups. Furthermore, the fruit quality of the heat treatment group decreased, with a lower soluble solid content (SSC) and titratable acidity (TA), and smaller fruits. Additionally, a higher fruit disorder (cracking and spots) ratio was observed in the heat treatment group than in the water irrigation and control groups. However, the fruits of the water irrigation group exhibited higher quality indexes (flesh firmness, SSC, and TA) and less cracking than those of the heat treatment and control groups. Heat reduction, including water irrigation, may be used for orchard management to prevent climate change-induced increasing temperatures. Full article

The percent canopy cover and health of winter wheat are important crop performance indicators. Thus, understanding how tillage management practices affect these indicators is beneficial for improving crop performance and consequently yield. The availability of high-resolution drone data with spectral characteristics provides an opportunity to examine the percent canopy cover and health of winter wheat in different tillage systems. This is because the use of drones provides real-time high spatial resolution and temporal images to effectively monitor winter wheat conditions throughout the growing season. Nonetheless, very limited studies have utilized drone data for assessing the percent canopy cover and health conditions of winter wheat for different tillage practices. This study aimed to examine the percent canopy cover and health of winter wheat in no-till and conventional tillage plots using a drone. We used the mean Normalized Difference Vegetation Index (NDVI) ± Standard Deviation (SD) (0.89 ± 0.04) of winter wheat for the growth stages of tillering, jointing, and boot/heading to generate the percent wheat canopy cover. The Normalized Difference Red-Edge (NDRE) produced for winter wheat at the middle and late growth stages was used as a proxy for wheat health condition. We found that the mean percentage canopy cover of winter wheat was about 4% higher in no-till compared to conventional tillage plots in most of the growing season. The mean NDRE ± standard error (SE) of winter wheat was about 0.44 ± 0.01 and 0.43 ± 0.01 for no-till and conventional tillage plots, respectively, during the mid- and late growth stages. There was no significant difference in either the percent canopy cover or health of winter wheat between no-till and conventional tillage plots. The results generated in this study could be used to support farmers’ decision-making process regarding tillage practices and wheat crop performance. Full article

Nitro-substituted neonicotinoid insecticides have been widely used until recently to control a range of important agricultural pests. Growing concerns about thiamethoxam’s toxicity to pollinators have led to its use being restricted or to it even being banned in some countries. Nevertheless, in Asia, Africa, Southeast Europe, and South America thiamethoxam is still used. Although thiamethoxam has been intensively studied all over the world, its dissipation dynamics have not been studied in depth. The subject of the present study was to (1) develop and validate a QuEChERS/LC-MS/MS protocol for the determination of thiamethoxam and its main metabolite clothianidin in samples of young oilseed rape plants with high chlorophyll content, and (2) make a comparison of the degradation behaviors of thiamethoxam and clothianidin in two crops of winter oilseed rape, cultivated on soils with different pH. For determination of thiamethoxam and clothianidin in plant material with high chlorophyll content, a QuEChERS/LC–MS/MS protocol enabling the detection of low levels of compound concentrations was developed. The proposed clean-up protocol provided recoveries within the range of 92–98% for the compounds under analysis. Precision, calculated as relative standard deviation, was below 20%. Satisfactory linearity of the method was obtained in the concentration range under analysis (0.001–1.0 mg kg⁻¹). Differences in degradation of both insecticides, depending on the physico-chemical properties of the soil, were observed. Thiamethoxam and clothianidin residues disappeared in plants very quickly, and they were not detected below the limit of quantitation in oilseed rape at the flowering stage. Full article

Using beneficial microorganisms, such as purple non-sulfur bacteria (PNSB), has shown enormous potential for improving plant growth and agricultural production. However, the full extent of their benefits and interactions with agricultural practices is yet to be fully understood. The present study aimed to investigate the use of PNSB in crop rotation practice, focusing on its impact on rice growth and yield. The experiment was conducted over two rice cropping seasons, with djulis grown between the rice as a rotation crop. The study shows that PNSB treatment increased the concentration of 5-aminolevulinic acid (5-ALA) in plants, indicating enhanced photosynthesis. Moreover, when combined with crop rotation, PNSB remarkably improved soil fertility. These combined benefits resulted in substantial increases in tiller numbers (163%), leaf chlorophyll content (13%), and lodging resistance (66%), compared to the untreated plants. The combined treatment also resulted in higher productive tillers per hill (112%), average grain per hill (65%), and grain fertility (26%). This led to increased grain yield (65%), shoot dry weight (15%), and harvest index (37%). The findings clearly suggest that the incorporation of PNSB in crop rotation strategies can significantly augment the growth and yield of rice crops. These insights, pivotal for sustainable rice cultivation, hold the potential to simultaneously tackle the pressing issues of global food security and climate change. Full article

(1) Background: The effective selection of hyperspectral feature bands is pivotal in monitoring the nutritional status of intricate alpine grasslands on the Qinghai–Tibet Plateau. The traditional methods often employ hierarchical screening of multiple feature indicators, but their universal applicability suffers due to the use of a consistent methodology across diverse environmental contexts. To remedy this, a backward feature elimination (BFE) selection method has been proposed to assess indicator importance and stability. (2) Methods: As research indicators, the crude protein (CP) and chlorophyll (Chl) contents in degraded grasslands on the Qinghai–Tibet Plateau were selected. The BFE method was integrated with partial least squares regression (PLS), random forest (RF) regression, and tree-based regression (TBR) to develop CP and Chl inversion models. The study delved into the significance and consistency of the forage quality indicator bands. Subsequently, a path analysis framework (PLS-PM) was constructed to analyze the influence of grassland community indicators on Spec_Chl and Spec_CP. (3) Results: The implementation of the BFE method notably enhanced the prediction accuracy, with ΔR²_RF-Chl = 56% and ΔR²_RF-CP = 57%. Notably, spectral bands at 535 nm and 2091 nm emerged as pivotal for CP prediction, while vegetation indices like the PRI and mNDVI were critical for Chl estimation. The goodness of fit for the PLS-PM stood at 0.70, indicating the positive impact of environmental factors such as grassland cover on Spec_Chl and Spec_CP prediction (r_Chl = 0.73, r_CP = 0.39). Spec_Chl reflected information pertaining to photosynthetic nitrogen associated with photosynthesis (r = 0.80). (4) Disscusion: Among the applied model methods, the BFE+RF method is excellent in periodically discarding variables with the smallest absolute coefficient values. This variable screening method not only significantly reduces data dimensionality, but also gives the best balance between model accuracy and variables, making it possible to significantly improve model prediction accuracy. In the PLS-PM analysis, it was shown that different coverage and different community structures and functions affect the estimation of Spec_CP and Spec_Chl. In addition, Spec_Chl has a positive effect on the estimation of Spec_CP (r = 0.80), indicating that chlorophyll does reflect photosynthetic nitrogen information related to photosynthesis, but it is still difficult to obtain non-photosynthetic and compound nitrogen information. (5) Conclusions: The application of the BFE + RF method to monitoring the nutritional status of complex alpine grasslands demonstrates feasibility. The BFE filtration process, focusing on importance and stability, bolsters the system’s generalizability, resilience, and versatility. A key research avenue for enhancing the precision of CP monitoring lies in extracting non-photosynthetic nitrogen information. Full article

Mining and metallurgy are the main sources of soil contamination with harmful metals, posing a significant threat to human health and ecosystems. River floodplains in the vicinity of metal mines or industrial plants are often subject to flooding with sediments containing heavy metals, which can be harmful to the soil ecosystem. This study aimed to investigate the microbial properties of the soil at a metal-contaminated site and to determine the significant relationships between the biological and chemical properties of the soil. The study site was located near the village of Gyöngyösoroszi, in the Mátra mountain region of Northwest Hungary. A phytoremediation experiment was conducted in a metal-polluted floodplain using willow and corn plantations. The soil basal respiration, substrate-induced respiration, soil microbial biomass carbon (MBC), acid phosphatase activities, and soil chemical properties were measured. The soil of the contaminated sites had significantly higher levels of As, Pb, Zn, Cu, Cd, and Ca, whereas the unpolluted sites had significantly higher levels of phosphorus and potassium. The substrate-induced respiration showed a positive correlation with MBC and negative correlations with the metabolic quotient (qCO₂). The soil plasticity index and phosphorus showed a positive correlation with MBC, whereas salinity and the presence of Cd, Pb, Zn, As, and Cu showed a negative correlation. Acid phosphomonoesterase activity negatively correlated with the plant-available phosphorus content and MBC, but was positively correlated with the contents of toxic elements, including cadmium, lead, zinc, arsenic, and copper. This study found a significant correlation between the qCO₂ and the toxic element content. This suggests that an enhanced metabolic quotient (qCO₂), together with a decreased MBC/SOC ratio, could be used to indicate the harmful effect of soil contamination by heavy metals in floodplain soils. Full article

Nitrogen (N) and sulfur (S) fertilization significantly affect seed yield and quality in Brassica oilseed crops. The effect of N and S management on the crop parameters (plant height, stem-base diameter, and number of branches), yield (seed yield components, seed and straw yields, harvest index—HI), and the quality of the seeds and oil (crude fat—CF, total protein—TP, crude fiber—CFR, fatty acids profile—FA, acid detergent fiber; and neutral detergent fiber) of oilseed radish (Raphanus sativus L. var. oleiformis Pers.) was analyzed in the study. The effect of N and S fertilization was evaluated in a field experiment in Bałcyny (north-eastern Poland) in 2020–2022. The experiment had a split-plot design with two factors and three replications. The first factor was the N rate (0, 30, 60, 90, 120 kg ha⁻¹) and the second factor was the S rate (0, 15, 30 kg ha⁻¹). Nitrogen fertilization stimulated stem elongation and branching. The average oilseed radish (OSR) seed yield ranged from 0.59 to 1.15–1.25 Mg ha⁻¹. Seed yields increased significantly, up to 90 kg N ha⁻¹ and 15 kg S ha⁻¹. The N fertilizer use efficiency (NFUE) of OSR decreased with a rise in the N rate (from 4.22 to 2.19 kg of seeds per 1 kg N). The application of S did not increase NFUE. The HI ranged from 10% (0–30 kg N ha⁻¹) to 12% (60 kg N ha⁻¹). The contents of CF, TP, and CFR in OSR seeds (kg⁻¹ dry matter—DM) were 383–384 g, 244–249 g, and 97–103 g, respectively. Nitrogen fertilization decreased the CF content (by 5%) and increased the contents of TP (by 5%) and CFR (by 16%) in OSR seeds. Sulfur fertilizer applied at 30 kg ha⁻¹ decreased the CF content (by 2%), but it did not alter the content of TP or CFR. Oilseed radish oil contained 68–70% of monounsaturated FAs (MUFAs) (erucic acid accounted for 2/3 of the total MUFAs), 24–25% of polyunsaturated FAs (PUFAs), and 6–8% of saturated FAs (SFAs). Nitrogen fertilization increased the proportions of SFAs and PUFAs in OSR oil. Nitrogen rates of 60–90 kg ha⁻¹ increased the contents of alpha-tocopherol (α-T), beta-tocopherol (β-T), and gamma-tocopherol (γ-T) in OSR seeds by 32%, 40%, and 27%, respectively. Sulfur fertilization increased the content of PUFAs and decreased the content of MUFAs in OSR oil, while it increased the contents of α-T (by 15%) and γ-T (by 19%) in OSR seeds. Proper N and S management in OSR cultivation can improve crop productivity and the processing suitability of seeds. Full article

(1) Background: This challenge is exacerbated by the aging of the rural population, leading to a scarcity of available manpower. To address this issue, the automation and mechanization of outdoor vegetable cultivation are imperative. Therefore, developing an automated cultivation platform that reduces labor requirements and improves yield by efficiently performing all the cultivation activities related to field vegetables, particularly onions and garlic, is essential. In this study, we propose methods to identify onion and garlic plants with the best growth status and accurately predict their live bulb weight by regularly photographing their growth status using a multispectral camera mounted on a drone. (2) Methods: This study was conducted in four stages. First, two pilot blocks with a total of 16 experimental units, four horizontals, and four verticals were installed for both onions and garlic. Overall, a total of 32 experimental units were prepared for both onion and garlic. Second, multispectral image data were collected using a multispectral camera repeating a total of seven times for each area in 32 experimental units prepared for both onions and garlic. Simultaneously, growth data and live bulb weight at the corresponding points were recorded manually. Third, correlation analysis was conducted to determine the relationship between various vegetation indexes extracted from multispectral images and the manually measured growth data and live bulb weights. Fourth, based on the vegetation indexes extracted from multispectral images and previously collected growth data, a method to predict the live bulb weight of onions and garlic in real time during the cultivation period, using functional regression models and machine learning methods, was examined. (3) Results: The experimental results revealed that the Functional Concurrence Regression (FCR) model exhibited the most robust prediction performance both when using growth factors and when using vegetation indexes. Following closely, with a slight distinction, Gaussian Process Functional Data Analysis (GPFDA), Random Forest Regression (RFR), and AdaBoost demonstrated the next-best predictive power. However, a Support Vector Machine (SVM) and Deep Neural Network (DNN) displayed comparatively poorer predictive power. Notably, when employing growth factors as explanatory variables, all prediction models exhibited a slightly improved performance compared to that when using vegetation indexes. (4) Discussion: This study explores predicting onion and garlic bulb weights in real-time using multispectral imaging and machine learning, filling a gap in research where previous studies primarily focused on utilizing artificial intelligence and machine learning for productivity enhancement, disease management, and crop monitoring. (5) Conclusions: In this study, we developed an automated method to predict the growth trajectory of onion and garlic bulb weights throughout the growing season by utilizing multispectral images, growth factors, and live bulb weight data, revealing that the FCR model demonstrated the most robust predictive performance among six artificial intelligence models tested. Full article

The emission factor method (Tier 1) recommended by the Intergovernmental Panel on Climate Change (IPCC) is commonly used to estimate greenhouse gas (GHG) emissions from livestock and poultry farms. However, the estimation accuracy may vary due to practical differences in manure management across China. The objectives of this study were to estimate the direct and indirect nitrous oxide (N₂O) emissions from dairy manure management between 1990 and 2021 in China and characterize its spatial and temporal variations following IPCC guideline Tier 2. The N₂O emission factor (EF) of dairy cow manure management systems was determined at the national level and regional level as well. The results showed that the national cumulative N₂O emission of manure management from 1990 to 2021 was 113.1million tons of CO₂ equivalent, ranging from 90.3 to 135.9 million tons with an uncertainty of ±20.2%. The annual EF was 0.021 kg N₂O-N (kg N)⁻¹ for total emissions, while it was 0.014 kg N₂O-N (kg N)⁻¹ for direct emissions. The proportions of N₂O emissions in North China, Northeast China, East China, Central and Southern China, Southwest China and Northwest China were 32.3%, 18.6%, 11.4%, 5.8%, 6.1% and 25.8%, respectively. In addition, N₂O emissions varied among farms in different scales. The respective proportions of total N₂O emissions from small-scale and large-scale farms were 64.8% and 35.2% in the past three decades. With the improvement in farm management and milk production efficiency, the N₂O emissions per unit mass of milk decreased from 0.77 × 10⁻³ kg to 0.48 × 10⁻³ kg in 1990–2021. This study may provide important insights into compiling a GHG emission inventory and developing GHG emission reduction strategies for the dairy farming system in China. Full article

In order to precisely reproduce the precise seeding process of the population in the air-suction seed-metering device, it is necessary to execute accurate modeling of seed particles using the bonded-particle model, in combination with the discrete element method (DEM) and computational fluid dynamics (CFD). Through the repose angle, slope screening, rotating container, and particle sedimentation experiments, in this paper, the influence of the filling accuracy of the bonded-particle model on the flow behavior and mixing characteristics of the seed population was first explored based on EDEM software. The viability of the suggested modeling approach for pelleted vegetable seeds, as described in this study, was confirmed by comparing experimental and simulation outcomes. The surface roughness values obtained from the studies above were utilized to assess the accuracy of the bonded-particle model in filling. Additionally, a mathematical technique for determining the surface roughness was provided. Furthermore, an analysis of the multiple contacts in the bonded-particle model was also performed. The results indicated that the simulation results closely matched the experimental data when the number of sub-spheres in the bonded-particle model was equal to or more than 70, as measured by the standard deviation. In addition, the most optimal modeling scheme for the pelletized vegetable seed bonded-particles, based on the cost of coupling simulation, was found to be the bonded-particle surface roughness (BS) with a value of 0.1. Ultimately, a practical example was utilized to demonstrate the utilization of the pelleted vegetable seed bonded-particle model and the DEM-CFD coupling approach in analyzing the accuracy of the seeding process in the air-suction seed-metering device. This example will serve as a valuable reference point for future field studies. Full article

As strawberries are a widely grown cash crop, the development of strawberry fruit-picking robots for an intelligent harvesting system should match the rapid development of strawberry cultivation technology. Ripeness identification is a key step to realizing selective harvesting by strawberry fruit-picking robots. Therefore, this study proposes combining deep learning and image processing for target detection and classification of ripe strawberries. First, the YOLOv8+ model is proposed for identifying ripe and unripe strawberries and extracting ripe strawberry targets in images. The ECA attention mechanism is added to the backbone network of YOLOv8+ to improve the performance of the model, and Focal-EIOU loss is used in loss function to solve the problem of imbalance between easy- and difficult-to-classify samples. Second, the centerline of the ripe strawberries is extracted, and the red pixels in the centerline of the ripe strawberries are counted according to the H-channel of their hue, saturation, and value (HSV). The percentage of red pixels in the centerline is calculated as a new parameter to quantify ripeness, and the ripe strawberries are classified as either fully ripe strawberries or not fully ripe strawberries. The results show that the improved YOLOv8+ model can accurately and comprehensively identify whether the strawberries are ripe or not, and the mAP50 curve steadily increases and converges to a relatively high value, with an accuracy of 97.81%, a recall of 96.36%, and an F1 score of 97.07. The accuracy of the image processing method for classifying ripe strawberries was 91.91%, FPR was 5.03%, and FNR was 14.28%. This study demonstrates the program’s ability to quickly and accurately identify strawberries at different stages of ripeness in a facility environment, which can provide guidance for selective picking by subsequent fruit-picking robots. Full article

The process of hole seeding on the mulch during full-film double-row furrow corn planting faces issues such as poor seed discharge and seed blockage. To address these challenges, a pendulum-lever-type hole-forming mechanism is designed, along with an adjustment device. By analyzing the working principles of the pendulum-lever-type hole seeder and the adjustment device, the structural parameters of the device are determined. Through theoretical analysis and simulation experiments, three-dimensional models of seeds and hole seeders are constructed. Based on MBD-DEM cosimulation, the trajectory of seed movement and the seeding process of the hole seeder are analyzed to elucidate the effects of the hole-former opening and the number of pendulum bearings on seeding quality. To improve the operational performance of the hole seeder, experiments are conducted using the hole seeder’s rotating disc speed, lever angle of the hole-former, and the number of pendulum bearings as experimental factors, with the qualification index, miss-seeding index, and reseeding index as experimental indicators. A three-factor, three-level Box–Behnken central composite experiment is performed to obtain mathematical models of the relationships between the experimental factors and indicators. Using Design-Expert 12 software, the regression models are optimized for multiple objectives to obtain the optimal parameter combination: a seeder disc speed of 49 r/min (corresponding to a forward speed of 5.76 km/h), a lever angle of 131°, and four pendulum bearings. Under this optimal parameter combination, the qualification index is 91.70%, the miss-seeding index is 4.57%, and the reseeding index is 3.73%. Experimental validation of the seeding performance of the hole seeder under the optimal parameter combination is conducted. Bench tests show that the qualification index, miss-seeding index, and reseeding index are 90.53%, 5.60%, and 3.87%, respectively. Field tests demonstrate a qualification index of 89.13%, a miss-seeding index of 5.46%, and a reseeding index of 6.41%. The actual results are consistent with the optimized values, providing valuable insights for the design and performance optimization of hole seeders. Full article

In this study, ¹H-NMR spectroscopy coupled with chemometrics was applied to study the wine metabolome and to classify wines according to different grape varieties and different terroirs. By obtaining the metabolomic fingerprinting and profiling of the wines, it was possible to assess the metabolic biomarkers leading the classification (i.e., phenolic compounds, aroma compounds, amino acids, and organic acids). Moreover, information about the influence of the soil in shaping wine metabolome was obtained. For instance, the relationship between the soil texture and the content of amino acids and organic acids in wines was highlighted. The analysis conducted in this study allowed extraction of relevant spectral information not only from the most populated and concentrated spectral areas (e.g., aliphatic and carbinolic areas), but also from crowded spectral areas held by lowly concentrated compounds (i.e., polyphenols). This may be due to a successful combination between the parameters used for data reduction, preprocessing and elaboration. The metabolomic fingerprinting also allowed exploration of the H-bonds network inside the wines, which affects both gustatory and olfactory perceptions, by modulating the way how solutes interact with the human sensory receptors. These findings may have important implications in the context of food traceability and quality control, providing information about the chemical composition and biomolecular markers from a holistic point of view. Full article

Ongoing climate change is having a profound impact on agriculture, which is attracting attention from the scientific community. One of its effects is an increase in average temperature, which is a key factor in grape cultivation. This may increase the popularity of viticulture in central Europe. The aim of this study was to assess the potential for the development of viticulture in central Poland based on SAT changes from 1975 to 2021, in addition to changes in evapotranspiration, occurrence of late spring and early autumn frosts and frosty days in selected years from this period as an important factors relating to climate change. The research utilized data obtained from the Institute of Meteorology and Water Management—National Research Institute. The Bai–Perron test was used to determine the direction of temperature changes. An AR(1) autoregression model was used to predict SAT changes in central Poland for the years 2022–2026, based on the results of the Bai–Perron test. As part of the in-depth research on the SAT index, reference evapotranspiration calculations were also made as a second factor that is considered an important indicator of climate change. The Sum of Active Temperatures from 1975 to 2021 in the provinces of central Poland showed an increasing trend of 0.07% per year. The average SAT in central Poland in 2022–2026 is expected to range from 2700 °C to 2760 °C. Considering the current thermal conditions in central Poland and the forecasts for the coming years, it can be expected that vineyard cultivation will develop in this region. However, the research shows that the observed increasing trend in evapotranspiration, both in total in individual years and in the period of the greatest vegetation, i.e., in the months from May to the end of August, will result in an increasing need in central Poland to ensure adequate irrigation in developing vineyards. Full article

Power shift tractors have been widely used in agricultural tractors in recent years because of their advantages of uninterrupted power during shifting, high transmission efficiency and high stability. As one of the indispensable driving states of the power shift tractor, the starting process requires a small impact and a starting speed that meets the driver’s requirements. In this paper, aiming at such contradictory requirements, the starting control strategy of a power shift tractor is formulated with the goal of starting quality and the driver’s intention. Firstly, the identification characteristics of the driver under three starting intentions are obtained by a real vehicle test. An extreme learning machine with fast identification speed and short training time is used to establish the basic driver’s intention identification model. For the instability of the identification results of the Extreme Learning Machine (ELM), the particle swarm optimization algorithm (PSO) is used to optimize the ELM. The optimized extreme learning machine model has an accuracy of 96.891% for driver’s intention identification. The wet clutch is an important part of the power shift gearbox. In this paper, the starting control strategy knowledge base of the starting clutch is established by a combination of bench tests and simulation tests. Through the fuzzy algorithm, the driver’s intention is combined with the starting control strategy. Different drivers’ intentions will affect the comprehensive evaluation model of the clutch (the single evaluation index of the clutch is: the maximum sliding power, the sliding power, the speed stability time, the impact degree), thus affecting the final choice of the starting clutch control strategy considering the driver’s intention. On this basis, this paper studies and establishes the MPC starting controller for the power shift gearbox. Compared with the linear control strategy, the PSO-ELM-fuzzy weight starting strategy proposed in this paper can reduce the maximum sliding friction power by 45%, the sliding friction power by 69.45%, and the speed stabilization time by 0.11 s. The effectiveness of the starting control strategy considering the driver’s intention proposed in this paper to improve the starting quality of the power shift tractor is verified. Full article

An automatic system for cantaloupe flower pollination in greenhouses is proposed to meet the requirements of automatic pollination. The system consists of a mobile platform, robotic manipulator, and camera that reaches the flowers to detect and recognise their external features. The main task of the vision system is to detect the position and orientation of the flower in Cartesian coordinates, allowing the manipulator to reach the pose and perform pollination. A comprehensive method to ensure the accuracy of the pollination process is proposed that accurately determines the position and orientation of cantaloupe flowers in real environments. The vision system is used to capture images, detect the flower, and recognise its state according to its external features, such as size, colour, and shape, thereby providing appropriate nozzle access during pollination. The proposed approach begins with a segmentation method designed to precisely locate and segment the target cantaloupe flowers. Subsequently, a mathematical model is used to determine the key points that are important for establishing the growth orientation of each flower. Finally, an inverse-projection method is employed to convert the position of the flower from a two-dimensional (2D) image into a three-dimensional (3D) space, providing the necessary position for the pollination robot. The experimental process is conducted in a laboratory and proves the efficacy of the cantaloupe flower segmentation method, yielding precision, recall, and F1 scores of 87.91%, 90.76%, and 89.31%, respectively. Furthermore, the accuracy of the growth-orientation prediction method reaches approximately 86.7%. Notably, positional errors in 3D space predominantly fall within the allowable range, resulting in a successful pollination rate of up to 83.1%. Full article

To improve our understanding of the selfing of G. lingzhi basidiospore monokaryons and increase the efficiency of breeding excellent strains, 52 basidiospore monokaryons were isolated from a commercial G. lingzhi strain (laboratory number P). A severe partial segregation was observed using the chi-square test, the growth rate of the monokaryotic strains was normally distributed, and colonies exhibited 5 forms. The genetic diversity of the monokaryotic strains was further demonstrated by intersimple sequence repeat (ISSR) analysis, and the similarity coefficient was in the range of 0.49–1, which was consistent with the genotype classification results. In total, 14 AxBx monokaryotic strains were randomly selected for selfing with the 1 AyBy strain when the similarity coefficient was 0.76, and a total of 14 offspring were obtained via selfing, all of which were incompatible with their parents. The traits of the selfing progenies were diverse. The mycelial growth rate, fruiting body yield, and polysaccharide, triterpene, and sterol contents were the main indices. According to the membership function value, 71.43% of the selfing progeny were super parent, and the A88 strain with the best comprehensive traits was selected. These findings prove that ISSR molecular marker-assisted breeding reduces blindness, greatly reduces workload, and improves work efficiency. Full article

Anthocyanins are precious industrial raw materials. Purple corn is rich in anthocyanins, with large variation in their content between organs. It is imperative to find a rapid and non-destructive method to determine the anthocyanin content in purple corn. To this end, a field experiment with ten purple corn hybrids was conducted, collecting plant images using a digital camera and determining the anthocyanin content of different organ types. The average values of red (R), green (G) and blue (B) in the images were extracted. The color indices derived from RGB arithmetic operations were applied in establishing a model for estimation of the anthocyanin content. The results showed that the specific color index varied with the organ type in purple corn, i.e., AC_CR for the grains, BRT for the cobs, AC_CB for the husks, R for the stems, AC_CB for the sheaths and BRT for the laminae, respectively. Linear models of the relationship between the color indices and anthocyanin content for different organs were established with R² falling in the range of 0.64–0.94. The predictive accuracy of the linear models, assessed according to the NRMSE, was validated using a sample size of 2:1. The average NRMSE value was 11.68% in the grains, 13.66% in the cobs, 8.90% in the husks, 27.20% in the stems, 7.90% in the sheaths and 15.83% in the laminae, respectively, all less than 30%, indicating that the accuracy and stability of the model was trustworthy and reliable. In conclusion, this study provided a new method for rapid, non-destructive prediction of anthocyanin-rich organs in purple corn. Full article

Sea buckthorn has garnered significant attention owing to its nutritional richness; however, it has a limited shelf life. In this study, the drying process of sea buckthorn was categorized into the first-, second-, and third-drying stages. Regression models were employed to examine the effects of the drying temperature, relative humidity of the medium, and prolonged high humidity retention on various parameters during the first- and second-drying stages. Comparative analysis revealed that the optimal drying conditions for the first-drying stage of sea buckthorn were a drying temperature of 80 °C, relative humidity of 28%, and high humidity retention time of 84 min. In the second-drying phase, the optimal conditions were a drying temperature of 78 °C, a relative humidity of 17%, and a high humidity retention time of 84 min. One-way optimization revealed that the optimal drying temperature for the third-drying stage was 70 °C. The implementation of temperature- and humidity-controlled infrared hot-air drying (TH-IRHAD) techniques considerably improved the outcomes. Specifically, the drying time, energy consumption, and degree of browning decreased by 34.43%, 36.29%, and 21.43%, respectively, whereas the brightness, rehydration ratio, total flavonoid content, and total phenol content increased by 8.94%, 16.99%, 20.57%, and 28.32%, respectively. Staged TH-IRHAD substantially reduced the drying duration, increased the efficiency, and enhanced the drying quality. Full article

Bidens tripartita L. is a cadmium (Cd) accumulator. However, the real-time influx or efflux of Cd²⁺ around its root apex has not yet been performed. The object of this experiment was to compare the roles of added ions in solution on dynamic Cd extraction by B. tripartita root tip. Quartz sand was used to grow the seedling of B. tripartite. The Cd concentrations of all samples were determined by using ICP-OES after digestion. The Cd²⁺ influx around the root apex was measured in vivo, i.e., using non-invasive micro-test technology (NMT). The results showed that the Cd²⁺ influx was found to be decreased by 35.9%, 43.7%, 20.6%, and 57.5% under 10 μM Cd combined with high content Ca²⁺, Mg²⁺, Fe³⁺, or K⁺ (16 mM, 8 mM, 0.5 mM, 18 mM, respectively), compared to that under 10 μM Cd stress. But Cd treatments with low content ions with 0.05 mM Fe³⁺ or 0.5 mM S increased the Cd²⁺ influx in roots by 20.5% and 34.6%, respectively. It was also found that Cd treatment with high concentrations of Ca²⁺ or K⁺ increased the shoot biomass of B. tripartita seedlings. Chl a and b contents were significantly decreased in the Cd treatments with low concentrations of Fe³⁺ or S compared to those under Cd stress alone, and the dehydrogenase activity of the roots decreased in the treatment of Cd with 0.05 mM Fe³⁺ or 0.5 mM S. Our results indicate that the addition of 0.05 mM Fe³⁺ or 0.5 mM S promoted Cd²⁺ influx and Cd uptake by B. tripartita. Unlike traditional measurement, the Cd²⁺ movements of three-dimensional space around the B. tripartita root tip had been performed by NMT. It was suggested that the effects of S and Fe³⁺ on the remediation potential of B. tripartita need to be further researched in the future. The results of this study provided a real-time and micro-dynamic theoretical basis for phytoremediation mechanisms. Full article

Aphis gossypii Glover is an important pest in cotton plantations. Medicago sativa L. (alfalfa) is a host plant for the aphid Aphis craccivora Koch and may prove to be an important reservoir of natural enemies to combat this pest. The objective of this study was to analyze the impact of different mowing frequencies of alfalfa traps on A. gossypii and their natural enemies, using both ground survey data and UAV remote sensing data. The alfalfa was mowed twice to facilitate the transfer of this primary natural enemy to the cotton fields. Ground surveys were carried out every five days to gather data, while temporal niche and niche overlap methods were used for further analysis. Findings collected over a period ranging from day 31 to day 91 indicated that compared to their counterparts with no alfalfa traps, the cotton fields containing these pest control measures demonstrated a reduction in the A. gossypii population of approximately 16%. A survey conducted 5 days after mowing the alfalfa on days 61 and 71 found that the cotton fields with alfalfa traps experienced a 24.14% and 26.09% reduction in A. gossypii numbers. In contrast, the cotton fields without alfalfa traps experienced a 76.92% and 55.08% increase in cotton aphid numbers during the same period. It is noteworthy that the cotton fields with alfalfa traps showed a delayed onset of cotton aphid damage of approximately 5 days compared to the fields without alfalfa traps. This discovery has significant implications for understanding the ecological control mechanism of A. gossypii within alfalfa traps. Planting alfalfa traps around fields in Xinjiang could be promoted as a method to prevent and control aphid damage. Full article

Effective management of lactating sows significantly influences various aspects of swine production. This study compared the impact of an hourly feeding regime and a five-times-daily feeding regime on the productive performance, body condition, and welfare of lactating sows, as well as on the growth performance of their offspring. Twenty-eight lactating sows (Landrace × Yorkshire) were divided into two groups: Group 1 was fed five times a day, and Group 2 was fed according to an hourly regime. The data were analyzed using independent-samples T-tests and the Mann–Whitney U test using Statistical Analysis System (SAS, 2011, Version 9.3) software. An hourly feeding regime positively affected (p < 0.05) sows’ feed intake and body condition, significantly reducing the days from the weaning-to-estrus interval. Group 1 exhibited significantly higher (p < 0.05) reductions in backfat thickness (BFT) and body condition score (BCS) during the weaning period compared to Group 2. Additionally, significant differences (p < 0.05) were observed in regard to sow body weight loss, feed intake, piglet livability and mortality rate at weaning, sow index, and calculated milk yield. Feeding sows according to an hourly regime positively impacted their productive performance compared to those fed five times daily. No significant differences (p > 0.05) were recorded in regard to the total number of piglets born, live births, mummified piglets, stillbirths, piglet mortality, litter size at weaning, and sow feed conversion ratio (FCR). However, the number of piglets weaned per sow per year (PSY) was numerically higher in Group 2 (p > 0.05). The piglets from Group 2 had significantly higher (p < 0.05) weaning weights and exhibited lower feed intake, greater weight gain, improved average daily gain, and greater litter size weight gain than those from Group 1. Statistically, sows from Group 2 exhibited a higher frequency of standing (p < 0.05), which potentially contributed to the reduction in shoulder skin lesions in sows (p > 0.05). In conclusion, an hourly feeding regime could optimize sow productive performance, body condition, milk yield, welfare, and piglet growth in swine production. Full article

China’s small arched shed-building machinery suffers from a low degree of mechanization, building efficiency, and qualification rate for frame insertion. Therefore, we designed a rotary double-insertion device and established the equation for its motion trajectory. The analysis shows that in the rotary insertion process, a better point of entry into the soil exists. A simulation model was constructed in ADAMS, and the static and dynamic trajectories were analyzed. Additionally, the optimal planting and insertion speed ratios were determined. Considering the qualified rate of the insertion frame as the evaluation index to establish a regression model, we adopted a three-factor three-level experimental design and established the planting speed ratio, center distance of the planting arm, and length of the pressing rod arm as the main influencing factors. We used Design-Expert 13 to perform the analysis of variance and determined the optimal parameter combinations. The experimental results show that the planting speed ratio was 0.7, the center distance of the planting arm group was 554 mm, the length of the pressing rod arm was 923 mm, and the qualification rate of trellis planting at this time was 98.05%. The bench was adjusted and tested based on the optimal parameter combination. The average value of the measured trellis qualification rate was 96.73%, and the relative error between the test value and the theoretical optimization value was 1.32%, thereby verifying the reliability of the optimal parameter combination. Field verification test results show that the rotary double-insertion device had a planting speed ratio of 0.7 and a trellis qualified rate of 95.74% compared with the theoretical optimization value of 2.31%. Conforming to the design requirements of small arch shed-building machinery, the prototype operation performance was stable. Full article

Seed priming is routinely applied to improve germination rates and seedling establishment, but the decrease in longevity observed in primed seeds constitutes a major drawback that compromises long-term storability. The optimization of priming protocols able to preserve primed seeds from aging processes represents a promising route to expand the scope of seed priming. The present work explores this possibility in the model legume Medicago truncatula by testing the effectiveness of quercetin- and rutin-supplemented seed priming at improving the response to subsequent artificial aging. In comparison with a non-supplemented hydropriming protocol, supplementation with quercetin or rutin was able to mitigate the effects of post-priming aging by increasing germination percentage and speed, improving seed viability and seedling phenotype, with consistent correlations with a decrease in the levels of reactive oxygen species and an increase in antioxidant potential. The results suggest that quercetin and rutin can reduce the effects of post-priming aging by improving the seed antioxidant profiles. The present work provides novel information to explore the physiological changes associated with seed priming and aging, with possible outcomes for the development of tailored vigorization protocols able to overcome the storability constrains associated with post-priming aging processes. Full article

Synchronous motors have extended their presence in different applications, specifically in high-demand environments such as agronomy. These uses need advanced and better control strategies to improve energy efficiency. Within this context, sliding mode control has demonstrated effectiveness in electric machine control due to its advantages in robustness and quick adaptation to uncertain dynamic system disturbances. Nevertheless, this control technique presents the undesirable chattering phenomenon due to the discontinuous control action. This paper introduces a novel speed integral control scheme based on sliding modes for synchronous motors. This approach is designed to track smooth speed profiles and is evaluated through several numeric simulations to verify its robustness against variable torque loads. This approach addresses using electric motors for different applications such as irrigation systems, greenhouses, pumps, and others. Moreover, to address the chattering problem, different sign function approximations are evaluated in the control scheme. Then, the most effective functions for suppressing the chattering phenomenon through extensive comparative analysis are identified. Integral compensation in this technique demonstrates improvement in motor performance, while sign function approximations show a chattering reduction. Different study cases prove the robustness of this control scheme for large-scale synchronous motors. The simulation results validate the proposed control scheme based on sliding modes with integral compensation, by achieving chattering reduction and obtaining an efficient control scheme against uncertain disturbances in synchronous motors for agronomy applications. Full article

In order to meet the growing demand for food and achieve food security development goals, contemporary agriculture increasingly depends on plastic coverings such as agricultural plastic films. The remote sensing-based identification of these plastic films has gradually become a necessary tool for agricultural production management and soil pollution prevention. Addressing the challenges posed by the complex terrain and fragmented land parcels in karst mountainous regions, as well as the frequent presence of cloudy and foggy weather conditions, the extraction efficacy of mulching films is compromised. This study utilized a DJI Mavic 2 Pro UAV to capture visible light images in an area with complex terrain features such as peaks and valleys. A plastic film sample dataset was constructed, and the U-Net deep learning model parameters integrated into ArcGIS Pro were continuously modified and optimized to achieve precise plastic film identification. The results are as follows: (1) Sample quantity significantly affects recognition performance. When the sample size is 800, the accuracy of plastic film extraction notably improves, with area accuracy reaching 91%, a patch quantity accuracy of 96.38%, and an IOU and F1-score of 85.89% and 94.20%, respectively, compared to the precision achieved with a sample size of 300; (2) Different learning rates, batch sizes, and iteration numbers have a certain impact on the training effectiveness of the U-Net model. The most suitable model parameters improved the training effectiveness, with the highest training accuracy achieved at a learning rate of 0.001, a batch size of 10, and 25 iterations; (3) Comparative experiments with the Support Vector Machine (SVM) model validate the suitability of U-Net model parameters and sample datasets for precise identification in rugged terrains with fragmented spatial distribution, particularly in karst mountainous regions. This underscores the applicability of the U-Net model in recognizing plastic film coverings in karst mountainous regions, offering valuable insights for agricultural environmental health assessment and green planting management in farmlands. Full article

The segmentation and localization of Agaricus bisporus is a precondition for its automatic harvesting. A. bisporus growth clusters can present challenges for precise localization and segmentation because of adhesion and overlapping. A low-cost image stitching system is presented in this research, utilizing a quick stitching method with disparity correction to produce high-precision panoramic dual-modal fusion images. An enhanced technique called Real-Time Models for Object Detection and Instance Segmentation (RTMDet-Ins) is suggested. This approach utilizes SimAM Attention Module’s (SimAM) global attention mechanism and the lightweight feature fusion module Space-to-depth Progressive Asymmetric Feature Pyramid Network (SPD-PAFPN) to improve the detection capabilities for hidden A. bisporus. It efficiently deals with challenges related to intricate segmentation and inaccurate localization in complex obstacles and adhesion scenarios. The technology has been verified by 96 data sets collected on a self-designed fully automatic harvesting robot platform. Statistical analysis shows that the worldwide stitching error is below 2 mm in the area of 1200 mm × 400 mm. The segmentation method demonstrates an overall precision of 98.64%. The planar mean positioning error is merely 0.31%. The method promoted in this research demonstrates improved segmentation and localization accuracy in a challenging harvesting setting, enabling efficient autonomous harvesting of A. bisporus. Full article