To address challenges in seed feeding stability and seeding uniformity in agricultural practices, this study aimed to introduce a cylindrical air-assisted drill sowing device (CADSD) designed for rapeseed, wheat, and rice (RWR). The device features a prototype hill-feeding mechanism that addresses problems related to seed feeding, airflow disruptions, and seed–wall collisions. Comprehensive bench tests, Discrete Element Method (DEM) simulations, and preliminary field experiments were conducted to evaluate the seed-feeding stability characteristics and optimize the structural parameters of the air-assisted drill sowing system, enhancing seeding uniformity and operational efficiency. The optimal operating speed range is between 4 and 5 km/h. When the seed feeding speed is 30 to 38 r/min, the coefficient of variation of the seed supply rate stability is less than 0.55%, and the relative error between the theoretical and the experimental actual values of the RWR supply rate regression model is less than 2%, further supporting the effectiveness of the device. A preliminary field test revealed a seeding uniformity coefficient of variation (CV) of 3.44% and an emergence rate of 88%, closely aligning with the desired metrics. The CADSD effectively sows multiple crop types with improved precision and uniformity, handling diverse seed types and sizes without requiring equipment modifications, highlighting its innovative impact on agricultural technology in the precise seeding of RWR.

Achieving precise seed distribution is essential for optimizing crop yields and agricultural productivity. This study examines the impact of divider head geometry on seed distribution accuracy in pneumatic air seeder systems using rapeseed, wheat, and rice. Three custom-designed divider heads—funnel distributor (A1), closed-funnel distributor (A2), and cone-shaped distributor (A3)—were developed for an eight-furrow opener seeding system, each featuring eight outlets per opener. Bench tests at air pressures of 3, 3.5, 4, 4.5, 5, and 5.5 kPa and speeds of 4 and 5 km/h revealed significant variations in seed distribution accuracy among the designs. The A2 distributor demonstrated the lowest coefficient of variation (CV) across all seed types: 4.3%, 2.6%, and 6.95% for A1, A2, and A3 in wheat, respectively; 4.5%, 3.4%, and 6.2% in rice, respectively; and 0.3%, 0.1%, and 1.0% in rapeseed, respectively. Seed types also significantly influenced feed rate uniformity, with average CVs of 2.91% for rapeseed, 3.85% for rice, and 4.90% for wheat. CFD-DEM simulations validated the superior performance of the A2 distributor by analyzing flow fields and velocity distributions, showing reductions in CVs by 19.09–54.55% compared to A1 and A3. Thus, the A2 distributor was identified as the optimal design, significantly improving seeding uniformity across all seed types. In conclusion, this study provides critical insights for redesigning seed drill distribution heads to minimize turbulence in the seed–air mixture transport, enhancing seeding uniformity and increasing crop yields and agricultural productivity.

To address the problem of unstable seed filling and low seeding accuracy caused by poor seed flow in conventional peanut seed metering devices, a novel precision metering device based on a cam-swing link was developed. Using EDEM simulations, the capacity of different type hole installation positions to induce seed cluster disturbance was analyzed. A single-factor test and MBD–DEM coupled simulation were conducted to analyze the seeding performance. The simulation results indicate that when the type hole protrusion height was set to half the thickness of the seeding disc, seed cluster kinetic energy remained relatively stable, enhancing the capability to disturb seeds. As the seeding disc rotational speed increased from 10 to 40 rpm, the qualified index initially increased and then declined. Increasing the cluster wrap angle from 20° to 70° similarly led to a peak in the qualified index and a steady decrease in the missed index. Using the JPS-12 computer vision-based test platform, a second-order rotary orthogonal design was applied to evaluate the seeding performance. The experimental results show that when the seeding disc rotational speed was set at 26 rpm and the seed cluster wrap angle at 46°, the qualified index reached 89.95%, and the missed index was 4.04%. The average plant spacing of peanuts in field experiments was 137.82 mm. These results meet the operational requirements for precision peanut planting.

This article discusses the salt stress in strawberry seedlings under greenhouse conditions in summer. Spectral acquisition equipment was used to obtain spectral data, and the ambient and leaf temperatures were combined to model and analyze the relative chlorophyll content in the strawberry seedling leaves. Four different salt gradients were employed to culture the strawberry seedings: S1 (0 mmol/L NaCl), S2 (50 mmol/L NaCl), S3 (100 mmol/L NaCl), and S4 (150 mmol/L NaCl). The results indicated that the spectral curves of the strawberry seedlings in groups S3 and S4 began to differentiate after day 3 (D3), and their average canopy temperature increased by 2.5 °C and 3.1 °C, respectively. The performance of traditional machine learning models integrating leaf temperature improved by more than 80%. Under each stress treatment, the one-dimensional ResNet model integrated with leaf temperature performed the best, with root mean square and mean absolute errors below 1.7 and 1.5, respectively. These results highlight the potential of incorporating temperature as an additional factor to improve the accuracy of plant stress assessments. By integrating temperature with spectral data, the model enhances the ability to monitor plant health dynamically and provides a more comprehensive understanding of how environmental factors influence plant physiology.