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Shifting from traditional puddled transplanted rice to mechanized drilled-seeding, combined with rotary straw mixing and furrow irrigation, has proven to be a crucial strategy for reducing methane emissions while optimizing resource utilization and productivity. However, this technique introduces other challenges, such as furrow runoff and soil erosion, which contribute to the degradation of ridge/bed soil quality, fertilizer loss, and transport of agroecological pollutants. Therefore, this study explores a hybrid variable flux irrigation (VFI) model as a decision support tool to mitigate these limitations by controlling soil anoxia and runoff in furrow-irrigated rice after straw incorporation. The approach integrates the Hydrus 2D/3D with optimal loop controllers to adjust pump operations based on specific soil moisture levels, variable flux, and furrow water flow depths. Experimental validation and a field case study were conducted in Babaiqiao, Nanjing City, China, where rotary straw mixing and furrow-ridge layouts were applied alongside dry rice seeding and soil hydraulic experiments. The performance indicators of the Hydrus 2D/3D variable flux demonstrated reliable simulation of lateral wetting rates and soil moisture content with R2 of 0.79 and 0.89, corresponding RMSE values of 7.90 % and 7.60 %, and MRE values of 1.85 m/day and 0.07 cm³/cm³, respectively. The VFI model proved effective, indicating that the optimal irrigation schedule consisted of three distinct supply regimes. During each cycle, the pump operated intermittently, running for approximately 2.66 hours. VFI led to a 33 % reduction in pumping energy costs while simultaneously mitigating soil anoxia and furrow runoff, thus a potential to reduce the environmental footprint of rice-wheat rotations. Although rice yields under drilled seeding were reduced by 9.56 % compared to flood irrigation due to heavy weed infestation, the VFI model provides valuable insights for promoting straw incorporation and implementing practical solutions that support optimal water utilization and sustainable productivity.

A rotary tiller is a common tillage tool for straw incorporation in an intensive tillage system. However, rotary tillage for seedbed preparation in dense-straw mulching conditions experiences high torque and poor performance of straw incorporation. Nowadays, a great deal of studies have been focused on mimicking the morphological features of low-resistance animals to improve the performance of soil-engaging tools. Accordingly, the present study investigated the performance of three C-type rotary blades (i.e., conventional, serrated, and biomimetic) under three straw lengths (50, 100, and 150 mm) for incorporation of straw into the field using an in situ field tillage testing bench. Compared to the conventional and serrated blades, the biomimetic blade had lower straw displacement (decreased by an average of 50 mm and 7 mm, respectively), higher straw burying rate (increased by an average of 5.2% and 7.8%, respectively), better straw distribution (decreased by an average of 9.1% and 10.4% on the coefficient of variation, respectively), as well as lower torque and power (decreased by an average of 3.3 N·m and 4.4 N·m, respectively) under all straw lengths. The improved performance of the biomimetic blade could be attributed to the fact that its typical teeth configuration was designed by mimicking the smooth arc of the mole-rat’s claw. These results demonstrated that the biomimetic-designed blade could be a better option for incorporating dense straw into the field conditions.

As brown mushrooms are both delicious and beneficial to health, the global production and consumption of brown mushrooms have increased significantly in recent years. Currently, to ensure the quality of brown mushrooms, selective manual picking is required, and the delicate surface of the mushrooms must not be damaged during the picking process. The labor cost of picking accounts for 50–80% of the total labor cost in the entire production process, and the high-humidity, low-temperature plant environment poses a risk of rheumatism for the laborers. In this paper, we propose a novel underactuated gripper based on a lead screw and linear bearings, capable of operating with flexible force control while simultaneously measuring the diameter of the mushrooms. The gripper features three degrees of freedom: lifting, grasping, and rotation, and enabling it to approach, grasp, and detach the mushroom. A thin-film force sensor is installed on the inner side of the fingers to achieve accurate grip force measurement. The use of a PID algorithm ensures precise grip force control, thereby protecting the brown mushrooms from damage. Experimental results demonstrate that the proposed gripper has a static grasping force error of 0.195 N and an average detachment force overshoot of 1.31 N during the entire picking process. The in situ measurement of the mushroom diameter achieves 97.3% accuracy, with a success rate of 98.3%. These results indicate that the gripper achieves a high success rate in harvesting, a low damage rate, and accurate diameter measurement.

In order to measure wheat yield and wheat spike phenotypes, the grain number of wheat spikes is counted manually at present, but acquiring the grain number of wheat spikes is laborious and time-consuming. Counting the grain number of wheat spikes with an image processing method is promising, yet the application of this method is flawed due to its low accuracy. In this work, images of wheat spikes were collected and processed with technical procedures, including image cropping, image graying, histogram equalization, image binarization, eroding operation, removing small objects, filling image holes, revolving vertical spikes, cutting off stems, and removing stems. Wheat stems in binary images were eliminated by the sum pixels method, and the morphological characteristic parameters of the image areas of wheat spikes and lengths of wheat spike axes were calculated. Mathematical models relating the image areas of wheat spikes and lengths of the wheat spike axes to the grain number were established, and the mathematical models were verified. The results showed that the characteristic parameters of the image areas of wheat spikes and the lengths of the wheat spike axes for the spike images were linear relative to the grain number, and the maximum determination coefficients R2 were 0.9336 and 0.9012, respectively. The maximum determination coefficients R2 for the practical and predicted grain numbers were 0.9552 and 0.9369, respectively, and the minimum average absolute error was 2.3, while the average relative error for the mathematical models was 5.65%. The mathematical models relating the image areas of wheat spikes and the lengths of the wheat spike axes to the grain number were practical and accurate, and the mathematical model comparing the image area of wheat spikes and the grain number was superior to that comparing the length of the wheat spike axis and the grain number. The grain number of wheat spikes could be acquired accurately and quickly by the image processing method extracting the characteristic parameters of wheat spikes.

The advancement of agricultural mechanization necessitates precise and standardized classification based on technical characteristics to enhance green, efficient, and high-quality development. The current lack of scientific and standardized definitions and classifications for various types of agricultural machinery has become a bottleneck, complicating the machine selection and affecting the compatibility of the machinery with optimized field operations. To address this complexity, we propose a comprehensive classification method that integrates principal component analysis (PCA), cluster analysis, and the qualitative analysis of the functional components for defining and scientifically classifying harvesters. The key functional and technical properties of harvesters were analyzed, and eight primary parameters (machine weight, cutting width, feed rate, rated power, overall machine length, width, height, and working efficiency) were selected, supplemented by nine key functional components (walking mechanism, cutting device, threshing device, separating device, cleaning device, grain collecting device, grain unloading device, cabin, and track size). In the first step, principal component analysis was performed to reduce the dimensionality of the parameters, yielding three principal components with contribution rates of 41.610%, 28.579%, and 15.134%, respectively. One primary parameter from each component was selected for further analysis. In the second stage, Q-type cluster analysis classified the harvesters based on the squared Euclidean distance between the operational parameters, resulting in three classes of harvesters. Finally, functional component analysis provided detailed insights, further refining the classification into four major categories: mini, small, medium, and large harvesters. The results of this work provide a scientific basis for the definition and classification of the harvester products available on the market. This method offers a robust framework for the rational selection and planning of agricultural machinery, promoting sustainable mechanization with a focus on technical parameters and functional attributes.

Explaining the physiological and ecological effects of wheat population density can provide new research methods for field crop production. A three-year field trial under drill single-seed seeding was conducted, which used three different intra-row seed-seedling spacings to quantitatively analyze the density effect from three perspectives—population, individual plant, and single-stem panicle—at the winter wheat harvest. The results showed that year and density had significant effects on both the population and individual plant yield (p < 0.05), as well as on some yield components and biomass indicators. The interaction between planting density and annual climate was found only in the number of grains for both the entire population and individual plants. With the increase in planting density, the CI gradually increased, inhibiting the growth of individual plants and leading to a negative impact on monoculture wheat yield. The drill single-seed seeding method can provide a basic experiment condition for analyzing the density effect. The density effect of wheat populations originates from intraspecific competition, which mainly affects the growth of individual plants. Research based on the analysis of density effects from the perspectives of population, individual plants, and single-stem panicles can provide a methodological reference for precision agriculture.