Current research on garlic clove breaking primarily focuses on equipment development and optimization, with limited attention to elucidating the relationship between the breaking process and resultant damage or clove separation. To address this gap, this paper presents GarlicNet, a deep learning model that utilizes pressure signals generated during clove breaking to accurately detect breakage severity and separation extent. The model employs an enhanced adaptive channel attention mechanism to capture critical multi-channel information and a dual-branch attention structure to amplify salient features within pressure signals. Experimental results demonstrate high performance: GarlicNet achieved 95.6% accuracy, 95.9% recall, 95.9% precision, and 95.8% F1-score for breakage detection; corresponding values for separation detection were 96.5%, 96.5%, 96.5%, and 96.4%. Ablation studies confirm the efficacy of the Dynamic Multi-Channel Convolution Fusion (DMCF) module and Dual-Branch Attention Fusion (DAF) structure. Compared to benchmark models, GarlicNet exhibits superior detection performance and robustness. These findings validate the feasibility of predicting breakage and separation via pressure signal during clove breaking and underscore the model’s practical utility. This approach shows significant potential for mechanized garlic processing by reducing losses, improving efficiency, and advancing industrial automation.

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 improve the efficiency and reduce the labor intensity of rhizobial inoculation, a variable-rate spraying and control system for variable rate application (VRA) was developed. This system uses an incremental Proportional Integral Derivative (PID) closed-loop control algorithm to accurately regulate the spraying rate according to the target application rate, making it particularly suitable for the precision spraying of small volumes. Laboratory tests on precise flow control showed the variation coefficient of the spraying nozzle did not exceed 1.2% within the pressure range of 0.10-0.20 MPa, and a clear linear relationship was observed between the spraying rate and pressure. For small-flow control, the maximum response time was 1.93 s, with an average of 1.62 s. Field trials at Heilongjiang Agricultural Xianghe Farm in China demonstrated that soybeans inoculated with rhizobia exhibited better average seed counts and 100-seed weights compared to a control group. When applied together with a full base fertilizer, the average yield with liquid rhizobial inoculant increased by 232.5 kg/hm², representing a 6.9% improvement over the control. These results clearly indicate that this spraying and control system for liquid rhizobial inoculation offers superior performance and provides important technical support for promoting widespread adoption of rhizobial technology in agricultural practice.

Genotyping and phenotyping are critical for wheat breeding, with accurate phenotypic acquisition from potted wheat using 3D point cloud technology essential to overcoming bottlenecks from slow, inefficient processes. This study focuses on developing methods for accurate organ-level phenotypic data extraction from potted wheat plants using 3D point cloud techniques. The study exploited a multi-view acquisition system to construct point cloud datasets for wheat key growth stages and trained a network. Semantic (organ classification) and instance (organ segmentation) segmentation were performed on potted wheat to extract organ-level wheat point clouds. However, the imbalance in point cloud proportions among different wheat organs caused low semantic segmentation accuracy, and the interference from awns caused significant distortion in ear point clouds after instance segmentation. To address these issues, a class-related sampling strategy based on RandLA-Net was proposed, which balances various organ point clouds through a class-related sampling strategy. In addition, a geometric symmetry-based point cloud completion method was introduced to replenish the distorted wheat ear point cloud. Based on the segmented organ point clouds, phenotypic parameters were obtained using minimum bounding box and quadratic surface fitting methods. The results showed that semantic segmentation accuracy improved by 13.6% through class point balancing, and the determination coefficient for the extracted ear volume increased by 10.2% after point cloud completion. The obtained phenotypic parameters showed a strong correlation with manual measurements (determination coefficients ranging from 0.7737 to 0.9552). These phenotype acquisition methods provide accurate and practical phenotypic acquisition, supporting wheat optimization and superior gene screening.

Weeds severely reduce rice yield and quality, making reliable in-field detection of weed and rice seedling essential for automated weed management. Although deep learning-based object detection techniques have shown significant potential in automatically distinguishing crops from weeds, existing models often suffer from large model sizes, high computational complexity, and insufficient feature extraction. To address these issues, this study proposes a lightweight multi-angle object detection model named MAL-YOLOv5 (Multi-Angle Lightweight YOLOv5), based on the YOLOv5 (You Only Look Once version 5) framework, which effectively reduces model complexity while maintaining detection performance. Specifically, the lightweight MobileNetV3 architecture is adopted to replace the original backbone network, significantly decreasing the number of parameters without compromising accuracy. Furthermore, the neck network of MAL-YOLOv5 is enhanced by integrating spatial and channel reconstruction convolution (SCConv) and a single-shot feature aggregation module (SCCSP), which reduces spatial and channel redundancies in the convolutional module, thereby compressing the neck network and improving feature representation. Additionally, a rotated bounding box with angular information is introduced for annotating and detecting rice seedling and weed, which effectively mitigates the interference from background and non-target objects, enabling more precise identification. Experimental results show that the precision, recall, and mAP of the MAL-YOLOv5 model are 93.1%, 91.9%, and 93.4%, respectively. Compared to YOLOv5s_obb, the MAL-YOLOv5 model reduces the number of parameters by 80.1% and computational cost in GFLOPs (Giga Floating-Point Operations) by 81.5%, significantly minimizing model size with only marginal loss in accuracy, conserving computational and storage costs while lowering the hardware requirements for intelligent mechanical weeding equipment.

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.

This study systematically investigated the evolution law of rheological properties in the anaerobic fermentation system of cow manure (CM) and corn straw (CS) mixtures at varying total solids (TS). Combined with computational fluid dynamics (CFD) simulations, the distribution characteristics of particles within fermentation system were revealed, illustrating the impact mechanisms of agitation strategies. Simulations demonstrated that higher agitation speeds and TS levels increase instantaneous power consumption while altering mixing and sedimentation dynamics. When TS was 6%, 120 r/min required more power than 100 r/min and 80 r/min, but 100 r/min reduced daily energy consumption by 4.30% and 3.19% compared to 80 r/min and 120 r/min. Under conditions of TS=6% and 8%, with maintained process stability, experimental groups increased energy output by 12.42% and 30.41% while reducing energy consumption by 13.60% and 3.39% versus control groups, demonstrating significant overall efficiency gains. Inversely, when TS was 10%, experimental groups decreased energy output by 0.34%, while increasing energy consumption. These findings prove that when TS is below 10%, optimized agitation strategies enable positive net energy output, establish an optimization scheme, and balance biogas production efficiency with economic feasibility.

Temporal upscaling of evapotranspiration (ET) is crucial for improving water use efficiency and water-saving irrigation management in crop production, playing a vital role in guiding farmland irrigation. This study investigated the water consumption patterns and evaluated different temporal upscaling methods for ET in drip irrigated grapevines in Northeast China’s cold region. Based on a three-year experiment (2018, 2020, 2021), four upscaling methods were examined: the evaporation fraction method (EF method), the improved evaporation fraction method (EF′ method), the crop coefficient method (K_c method), and the direct canopy resistance method (r_c method), applied to both instantaneous-to-daily and daily-to-whole-growth-period timescales. The results demonstrated that all key parameters, including evaporation fraction (EF), improved evaporation fraction (EF′), crop coefficient (K_c), and canopy resistance (r_c), exhibited stable values from 8:00-16:00 when upscaling ET from instantaneous to daily scales. Their mean standard deviations (SD) ranged from 0.08-0.21, 0.08-0.19, 0.10-0.18, and 41.43-137.72 s/m, respectively. Regarding the simulation of instantaneous to daily upscaling, the methods ranked as EF method>EF′ method>K_c method>r_c method. The EF method achieved optimal performance at specific times: 11:30 (shoot growth and the flowering period), 12:00 (fruit expansion), and 12:30 (maturity). For daily to whole growth period upscaling, all four methods performed best during the fruit expansion stage, maintaining the same performance ranking. The EF method consistently exhibited the smallest errors, with MAE values of 35.31 mm, 33.00 mm, and 42.97 mm, and RRMSE values of 12.21%, 11.40%, and 14.62% in 2018, 2020, and 2021, respectively. Therefore, the EF method is recommended for both upscaling ET from instantaneous to daily timescales and from daily to the entire growth period for drip-irrigated grapevines in Northeast China’s cold region. The findings not only enrich the analytical framework for agricultural hydrological processes, but also hold significant implications for implementing “hourly precision water management” in greenhouse grape cultivation and enhancing water use efficiency in greenhouse grape systems.

To mitigate the damage rate of Camellia oleifera seeds during the process of Camellia oleifera fruit dehulling, a Camellia oleifera fruit cutting and compression machine was developed. This machine operates on the principle that the shell-breaking stress of Camellia oleifera fruit is reduced following cutting, thereby integrating the steps of grading, cutting, compressing, and separating for the dehulling of Camellia oleifera fruit. Utilizing the electronic universal testing machine, a 4-factor single-factor test was conducted, including the number of cutting knives, cutting direction, compressing direction, and Camellia oleifera fruit size. The results showed that the average breaking force of Camellia oleifera seeds was 196.33 N. Compared with direct separating without cutting, the shell-breaking of Camellia oleifera fruit with 1, 2, 3, and 4 cuts decreased 30.14%, 38.62%, 45.05%, and 47.74%, respectively. Compared with cutting along the long axis, the shell-breaking force of Camellia oleifera fruit cutting along the minor axis decreased 8.1%; however, the effect of cutting direction on shell-breaking force is not significant. The shell-breaking force of Camellia oleifera fruit compressing along the long axis was 24.11% lower than that of Camellia oleifera fruit compressed along the minor axis. With the increase of Camellia oleifera fruit size, the shell-breaking force of Camellia oleifera fruit also increased. The shell-breaking force of 25-30 mm, 30-35 mm, and 35-40 mm was 85.62%, 127.34%, and 178.69% higher than that of 20-25 mm, respectively. In conclusion, using grading measures, making one cut along the long axis and compressing along the long axis is a suitable method for husking of Camellia oleifera fruit.

Olive leaves and pomace are being wasted in Pakistan and remain unexplored concerning their bioactivity and fatty acids. The objective of the present work is to study fatty acids and antioxidant potentials of olive leaves and fruit pomace indigenous to Pakistan. The antioxidant potential of both water extract (WE) and ethanol extract (EE) of olive leaves and fruit pomace was also investigated. Results showed that olive leaves exhibited the highest oleic acid content (54.60%±3.40%), followed by palmitic acid (15.20%±1.20%). For olive pomace, oleic acid was the most abundant fatty acid (59.30%±2.40%), while palmitic acid and linoleic acid were recorded at 6.30%±0.56% and 5.2%±0.61%, respectively. However, oleic acid (59.30%±2.40%) was present in the highest quantities in olive pomace, followed by palmitic acid (6.30%±0.56%) and linoleic acid (5.20%±0.61%). The EE of olive leaves had higher total phenolics (1902.00±189.00 mg GAE/100 g) than WE (1216.00±143.00 mg GAE/100 g). The WE of olive pomace had higher total phenolics (292.40±18.00 mg GAE/100 g) than EE (201.80±15.00 mg GAE/100 g). The scavenging activity of DPPH of ethanol-soluble extracts (EE) (60.60±3.50 µM TE/g) of olive leaves was also greater than their respective water-soluble extracts (WE) (51.20±3.00 µM TE/g). Similarly, scavenging activity of ABTS of EE (9.88±0.90 µM TE/g) of olive leaves was greater as compared to WE (3.24±0.40 µM TE/g) of leaves. The DPPH radical scavenging activity of EE (29.44±1.10 µM TE/g) of olive pomace was higher than its respective WE (14.24±1.40 µM TE/g). However, the ABTS scavenging activity of EE (6.05±0.90 µM TE/g) of olive pomace did not vary significantly (p<0.05) with regard to its respective WE (5.99±0.45 µM TE/g). Results concluded that olive leaves and olive pomace are good sources of fatty acids and antioxidants, which are recommended in food application according to a sustainable approach strategy.