Conventional seeding monitors often exhibit diminished accuracy under challenging field conditions. To address this, this study introduces a novel monitoring system leveraging flexible pressure sensors integrated with a finger-clamp seed metering device. The core principle is that the passage of each seed-clamping finger over the seed outlet generates a distinct, continuous pressure signal profile. A sophisticated Signal Feature Identification Algorithm (SFIA) was developed that transforms this raw signal data into a one-dimensional image for analysis. By employing binarization and bilateral filtering, the SFIA effectively suppresses noise from field vibrations and extracts key topographical features, enabling precise quantification of seeding events through peak detection. The complete system, implemented using LabVIEW and Python, was rigorously evaluated in field trials. Under conventional tillage, the system achieved an overall monitoring accuracy of 96.55%, with reseeding and missed seeding detection accuracies of 98.96% and 98.55%, respectively. Critically, it maintained high performance in challenging no-till conditions, demonstrating 95.46% overall accuracy, with 98.35% for reseeding and 98.42% for missed seeding detection. This research validates a pressure-based sensing approach as a robust alternative to traditional methods, presenting a new technological pathway for developing high-precision seeding monitoring systems resilient to common agricultural interferences.

To address the issue of insufficient accuracy and stability in pneumatic peanut planters during high-speed operations (above 8 km/h), this study designs a peanut precision planter based on combined positive and negative pressure, and improves its performance via multi-dimensional optimization. The research uses Shandong “Silihong” peanuts as the study subject, determining their geometric characteristics and designing the core parameters of the seed-metering disc: diameter of 255 mm, 21 suction holes, and suction hole diameter of 6.2 mm. A comparative simulation using EDEM discrete element method between the arc-shaped and spoke-wing groove seed disturbance structures shows that the spoke-wing groove structure increases the average seed speed by 17.0%, significantly improving the seed filling effect. A coupled Fluent-EDEM gas-solid simulation confirmed the optimal negative pressure to be 6.0 kPa, achieving a single-seed adsorption rate of 98.6%. Orthogonal experiments were performed to optimize the operating parameters, and the optimal combination with corresponding sowing performance indices was obtained. Field tests indicated that the performance of the planter meets the national standards for precision sowing when the operating speed is ≤12 km/h.

To address high cleaning losses caused by high impurity content and uneven distribution during rapeseed threshing, this study proposes a drum pneumatic-assisted threshing and separation method. Initially, by determining the suspension velocity characteristics of various threshed components, auxiliary blades were designed and integrated to construct a non-uniform gradient airflow field, generating a directional pneumatic transport effect. CFD simulations confirmed that this configuration induces a stable axial spiral airflow. Driven dynamically by this airflow, miscellaneous materials are guided to migrate orderly toward the rear of the concave screen, enabling active source-based regulation of the spatial-temporal flow behavior and component distribution. Multi-parameter correlation analysis indicated a significant negative correlation (coefficient = –0.88) between axial airflow and the average impurity ratio. Field experiments demonstrated that at a feed rate of 2.8 kg/s with auxiliary blades, the grain loss rate was reduced to 3.7%, compared with 6.3% without auxiliary blades, corresponding to a 41.3% reduction. This research demonstrates that axial airflow can effectively regulate threshed material at the source, providing a novel approach for high-efficiency and low-loss threshing and cleaning.

Yield monitoring is crucial for the agricultural sector, as it can be used to inform decisions on harvesting, storage, and transportation. Traditionally, several statistical methods and visual inspection techniques are employed to get an early estimate of the final yield of citrus, with the downside of being inaccurate, costly, and time-consuming. In recent years, there have been a lot of advancements in the fields of Artificial Intelligence (AI) and computer vision, providing opportunities to automate plenty of things in different domains, including agriculture. This research proposes a deep learning-based framework that leverages multiple Convolutional Neural Networks (CNN) to efficiently and effectively operate in real-world environments, using field data to provide accurate, improved yield estimates. A high-quality dataset, consisting of citrus tree images, is obtained from orchards at the university research farm Koont and the National Agriculture Research Center (NARC). Afterwards, the CNN-based models are trained thoroughly with various configurations and data augmentation techniques. All the models are rigorously tested and evaluated on the basis of a number of performance metrics. Experiments have shown that YOLOv8m performs with the highest mean average precision, reaching up to 90% with an inference time of a few milliseconds, making it worthy to be deployed for fruit detection, counting, and yield estimation tasks.

Onions, often referred to as the “Queen of the kitchen”, play a vital role in enhancing dishes with their distinctive flavor, phenols, and flavonoids. Despite their significance, the onion market faces considerable volatility due to seasonal variations and substantial postharvest losses, resulting in significant price fluctuations. These losses can reach up to 40%, primarily due to improper handling, sprouting, rotting, inadequate storage, and limited infrastructure, with microbial spoilage alone accounting for 15%-20% of the total losses. Conventional onion storage methods demand substantial investments but often fail to effectively manage temperature and relative humidity (RH) fluctuations, which are critical for maintaining bulb shelf life. This study focuses on developing an on-farm evaporative cooled onion storage structure (ECOSS) to provide a long-term storage solution for onions. Results demonstrated that the ECOSS maintained internal temperatures between 24.5°C-31.3°C (average 27.1°C) and suitable RH levels throughout the storage period, with minimum values between 63.7%-67.2% and maximum values between 74.1%-75.5%. Physiological loss in weight (PLW), sprouting, and rotting were lowest at the 0.91 m storage height compared to 1.82 m after 5 months of storage. Onion firmness decreased over time, with the lowest firmness observed at 1.82 m (14.68 N). The net profit per ton of onions stored from May to September was INR6021 for the ECOSS, compared to INR2553 for traditional onion storage structure (TOSS). The ECOSS offers a cost-effective and efficient solution for reducing postharvest losses and enhancing onion storage.

To address the persistent challenges of low harvesting efficiency and seasonal labor shortages in Camellia oleifera production, this study proposes a novel canopy vibration harvester driven by a five-bar PRRRP parallel mechanism configured to generate an epitrochoid excitation trajectory. Through analysis of the epitrochoid excitation trajectory and in accordance with parallel mechanism design principles, the PRRRP configuration was selected, considering structural layout, transmission system, motion performance, and singularity distribution. On this basis, a vibration harvesting device driven by the PRRRP mechanism was designed and developed. Kinematic and dynamic analyses, as well as workspace modeling, were conducted, and real-time position acquisition of the driving components was achieved through inverse kinematics solutions. The harvesting device includes dual servo motor-driven linear modules, articulated linkages, and an adjustable excitation frame fitted with excitation rods to engage the tree canopy. Using Camellia oleifera cultivar of Changlin No. 40 as the target crop, the device generated an epitrochoid trajectory with a vibration frequency of 7 Hz and an amplitude of 90 mm, delivering multidirectional excitation to the canopy. By adjusting tree size parameters, the vibration response of branches with different inclination angles was investigated. The results showed that average vibration response accelerations for branches inclined at 0°-30°, 30°-60°, and 60°-90° were 14.04 m/s², 21.88 m/s², and 21.27 m/s², respectively, providing a theoretical basis for branch pruning. Harvesting trials indicated an overall fruit removal rate of 75.10% and an overall bud shedding rate of 11.55%, showing higher fruit removal and lower bud shedding than a traditional canopy vibration device employing linear reciprocating excitation. These results confirm that epitrochoid-based, parallel-mechanism excitation markedly improves fruit detachment efficiency and reduces collateral damage to tree architecture. Our study provides both theoretical insights and practical guidance for the development of next generation mechanized harvesters for Camellia oleifera and other woody fruit crops.

Vegetable plug seedlings form root-substrate composites characterized by granular discreteness and cohesive bonding, which makes the direct measurement of contact parameters difficult and limits accurate simulation of transplanting processes. This study calibrated the key contact parameters and developed a discrete element method (DEM) model for broccoli plug seedling root-substrate composites by integrating physical experiments with EDEM simulations. Root shear tests, substrate angle of repose, sliding friction, and direct shear tests were performed to determine intrinsic mechanical properties. Using Plackett-Burman screening, steepest ascent, and Box-Behnken designs, the optimal combination of root static friction coefficient, critical stress, and bonding radius was obtained, with a relative error of only 0.70% between simulated and measured shear forces. For the substrate, the calibrated contact parameters of substrate-substrate and substrate-steel interactions yielded relative errors of 2.46% and 2.30%, respectively, while the simulated internal friction angle differed by only 2.64% from experimental values. The final composite model, validated through compression tests, showed a yield limit error of 4.22% and closely matched the deformation behavior observed in experiments. These results demonstrate that the proposed DEM model accurately captures the coupled mechanical behavior of flexible roots and cohesive substrates, providing a reliable tool for visual force analysis during transplanting and supporting the design optimization of seedling-picking and soil-seedling interaction mechanisms.

Nitrous oxide (N₂O), a long-lived greenhouse gas, is primarily produced in agricultural soils through biological nitrification and denitrification processes. However, the effects of soil salinity on nitrogen transformation processes remain insufficiently understood, hindering the development of effective nitrogen management in salt-affected farmlands. In this study, laboratory incubation experiments combined with a ¹⁵N stable isotope tracing technique were conducted to quantify the effects of salt stress on nitrification and denitrification rates and their contributions to N₂O emissions. The results showed that during the first week of incubation, slight and moderate salinity (NaCl contents of 0.04% and 0.10%; EC_1:5≤1.10 dS/m) enhanced both nitrification and denitrification rates, whereas strong salinity (0.20% NaCl; EC_1:5≥1.42 dS/m) inhibited these processes. In the first week of incubation, nitrification and denitrification contributed approximately 65%-70% and 30%-35% to the total N₂O emissions under 60% water-filled pore space, respectively. These results indicate that nitrification represents the predominant source of N₂O production in saline soils during the first week following fertigation. The findings suggest that nitrogen management practices for inhibiting the nitrification process (e.g., the addition of nitrification inhibitors in companion with fertigation) may mitigate N₂O emissions in saline fields. This provides a scientific basis for optimizing nitrogen management in saline soils and reducing greenhouse gas emissions.

Temperature is a critical factor influencing crop growth in controlled environment agriculture. Accurate regulation of air temperature within a greenhouse is essential for promoting optimal crop development and enhancing production efficiency. In this study, a Categorical Boosting model based on SMOTETomek mixed sampling method and improved Sparrow Search Algorithm (SMOTETomek-ISSA-CatBoost) was proposed to predict the categories of greenhouse temperature control modes. This study utilized historical temperature control mode data, which had been accumulated by cultivation experts through practical production and demonstrated effective in temperature management. To enhance the model’s performance and achieve real-time, precise temperature regulation in greenhouses, firstly, the SMOTETomek mixed sampling method was utilized to expand the original training set, effectively addressing the issue of data imbalance. Secondly, the Latin Hypercube Sampling (LHS) method, the Cauchy mutation perturbation operator, and a greedy rule were employed to refine the Sparrow Search Algorithm to enhance the global search capability. Ultimately, the improved Sparrow Search Algorithm was employed to optimize the hyper-parameters of CatBoost model to improve its predictive accuracy. Compared with SMOTETomek-CatBoost models optimized by Whale Optimization Algorithm (WOA), Fruit Fly Optimization Algorithm (FOA), Particle Swarm Optimization (PSO), and standard Sparrow Search Algorithm (SSA), the SMOTETomek-ISSA-CatBoost model demonstrated better prediction efficacy, with F1-score and AUC values reaching 0.8147 and 0.9629, respectively. The SMOTETomek-ISSA-CatBoost model exhibited the capability to predict the category of temperature control modes in a greenhouse accurately, thereby providing a decision-making foundation for intelligent management of greenhouse environments.

The compatibility between the morphological characteristics (MCs) of rapeseed bare-root seedlings and transplanters directly affects planting quality. To improve the adaptability of transplanters to the MCs of different rapeseed cultivars, this study focused on six winter rapeseed cultivars (A1: Huyou 17, A2: Huayouza 9, A3: Fengyou 520, A4: Zhongyou 108, A5: Zheyou 50, and A6: Huayouza 62). Five MCs—root length (RL), seedling height (SH), root diameter (RD), stem thickness (ST), and seedling width (SW)—were measured during the seedling ages (25-40 d). Multiple comparisons were conducted to identify cultivars with no significant differences (NSD, α=0.05) in MCs, while skewness and kurtosis were analyzed to assess temporal variations in MC distributions. Quadratic polynomial regression was employed to model the growth trends of MCs for each cultivar. The results showed that the skewness and kurtosis ranges of MCs were –0.65 to 1.16 and 1.62 to 6.28, respectively, indicating significant variability in growth symmetry and concentration both within and among cultivars. As seedling age increased, the number of cultivars with NSD in MCs progressively decreased (25 d: 5; 30 d: 4; 35 d: 3; 40 d: 2), but A3, A5, and A6 maintained consistent stability before 35 d. Based on MC statistical analysis, the key design parameters for the transplanter were determined as follows: flat belt width was 180 mm, positioning bar spacing was 70 mm, flexible belt width was 150 mm, clamping distance was 8 mm, seedling drop height was 180 mm, and planting depth was 40 mm. Field tests demonstrated that the transplanter exhibited superior compatibility with cultivars A3, A5, and A6, achieving seedling delivery success rates of 93.75% (A3), 93.23% (A5), and 92.71% (A6), along with significantly higher planting success rates compared to the control group (A1, A2, A4). This study provides a theoretical basis for optimizing the compatibility between transplanters and rapeseed bare-root seedlings, as well as guiding the structural design of transplanting machinery.