Search Results (5,168)

Whole-plant corn silage is a critical feedstuff in global ruminant production, and its nutrient composition is closely tied to harvest timing. As starch acts as the primary energy source in silage-based diets, investigating changes in starch degradation rate provides a theoretical basis for optimizing the efficient utilization of whole-plant corn and its silage in ruminant production. In this study, whole-plant corn (harvested from the milk stage to full ripening stage) and its corresponding silage were used as experimental materials. An in vitro simulated rumen fermentation system was employed to determine the contents of starch, prolamin, amylose, and amylopectin in the samples. The results showed that with delayed harvest time, starch content in both whole-plant corn and its silage increased significantly; prolamin and amylose contents first decreased, then increased; amylopectin content first rose significantly before decreasing; and both starch disappearance rate and speed exhibited a trend of first increasing, then decreasing. After silage fermentation, the silage had significant increases in starch, amylose, and amylopectin contents, and starch disappearance rate; prolamin content decreased; and starch disappearance speed increased extremely significantly. This study indicates that whole-plant corn harvest time and silage fermentation regulate the ruminal starch degradation pattern by altering starch structure, prolamin content, and the proportion of rapidly degradable starch. Full article

Early detection of pest damage and accurate assessment of fruit ripeness are essential for improving the quality, productivity, and sustainability of citrus production. Moreover, precisely assessing ripeness is crucial for establishing the optimal harvest time, preserving fruit quality, and enhancing yield. The simultaneous and precise early detection of pest damage and assessment of fruit ripeness greatly enhance the efficacy of contemporary agricultural decision support systems. This study presents a lightweight deep learning model based on an optimized YOLO12n-Seg architecture for the simultaneous detection of ripeness stages (unripe and fully ripe) and pest damage caused by Red Scale (Aonidiella aurantii). The model is based on an improved version of YOLO12n-Seg, where the backbone and head layers were retained, but the neck was modified with a GhostConv block to reduce parameter size and improve computational efficiency. Additionally, a Global Attention Mechanism (GAM) was incorporated to strengthen the model’s focus on target-relevant features and reduce background noise. The improvement procedure improved both the ability to gather accurate spatial information in several dimensions and the effectiveness of focusing on specific target object areas utilizing the attention mechanism. Experimental results demonstrated high accuracy on test data, with mAP@0.5 = 0.980, mAP@0.95 = 0.960, precision = 0.961, and recall = 0.943, all achieved with only 2.7 million parameters and a training time of 2 h and 42 min. The model offers a reliable and efficient solution for real-time, integrated pest detection and fruit classification in precision agriculture. Full article

White clover (Trifolium repens L.) is known for its ability to fix nitrogen biologically, its high nutritional value, and its adaptability to livestock systems. However, excessive fertilization with synthetic nitrogen alters its symbiosis with Rhizobium and reduces the protein content of the forage. The objective of this study was to evaluate the interaction between nitrogen fertilization (0 and 60 kg N ha⁻¹), cutting time, and post-cutting evaluation on the morphology, yield, and nutritional composition of white clover. A completely randomized block experimental design with three factors, distributed in three blocks, was used. Within each block, three replicates of each treatment were assigned (six interactions), giving a total of 54 experimental units. The data were analyzed using a three-way analysis of variance and Tukey’s multiple comparison test. Exponential models and generalized additive models (GAMs) were applied to the morphology and yield data to identify the best fit. The treatment with 60 kg N ha⁻¹ and cutting at 30 days showed significant increases in plant height (47.42%), fresh weight (59.61%), dry weight (98.41%), and leaf width (27.55%) compared to the control. It also produced the highest protein content (28.44%) compared to the other treatments with fertilization, without negatively affecting digestibility. The GAMs best fit most morphological and yield parameters (except leaf height and width). All fertilized treatments had higher fresh and dry weight yields. In conclusion, applying 60 kg N ha⁻¹ after cutting at 30 days, followed by harvesting between 54 and 60 days, improved both the quality and yield of white clover, which favored sustainable pasture management and reduced excessive nitrogen use. Full article

The g-C₃N₄/ZnO nanocomposite materials were applied to degrade methylene blue (MB). The samples were characterized and evaluated to study the adsorption and photocatalytic degradation under visible light. The g-C₃N₄ was incorporated at percentages of 5%, 10%, 20%, and 40% relative to the ZnO weight. These composite materials were prepared using a solvothermal microwave technique. The structural, textural, morphological, and optical properties were investigated using XRD, FTIR, SEM, EDS, STEM, BET, UV-Vis, and XPS techniques. The XRD patterns of the samples showed the coexistence of crystalline phases of g-C₃N₄ and ZnO, while images and elemental composition analysis confirmed the formation of nanocomposite samples. The UV-Vis spectrum revealed a redshift in the absorption edge of the nanocomposites, indicating improved light-harvesting capability. The synthesized material g-C₃N₄/ZnO (20/80), with a surface area of 25 m²/g, exhibited higher photocatalytic performance, achieving 85% degradation of MB after 100 min under visible light, which corresponds to nearly three times the degradation efficiency of commercial P25-TiO₂ (31%) under the same conditions. The reusability and stability tests were conducted up to the fifth cycle, and this material showed 77% degradation, indicating good stability. This nanocomposite material has good potential as a photocatalyst for solar-driven MB. Full article

Emerging power electronic devices like soft open points (SOPs) and electric springs (ESs) play a vital role in enhancing active distribution network (ADN) efficiency. SOPs enable flexible active/reactive power control, while ESs improve demand-side management and voltage regulation. This paper proposes a two-stage stochastic programming model to optimize ADN’s operation by coordinating these fast-response devices with legacy mechanical equipment. The first stage determines hourly setpoints for conventional devices, while the second stage adjusts SOPs and ESs for intra-hour control. To handle ES nonlinearities, a hybrid data–knowledge approach combines knowledge-based linear constraints with a data-driven multi-layer perceptron, later linearized for computational efficiency. The resulting mixed-integer second-order cone program is solved using commercial solvers. Simulation results show the proposed strategy effectively reduces power loss by 42.5%, avoids voltage unsafety with 22 time slots, and enhances 4.3% PV harvesting. The coordinated use of SOP and ESs significantly improves system efficiency, while the proposed solution methodology ensures both accuracy and over 60% computation time reduction. Full article

The traditional detection of blueberry maturity relies on human experience, which is inefficient and highly subjective. Although deep learning methods have improved accuracy, they require large models and complex computations, making real-time deployment on resource-constrained edge devices difficult. To address these issues, a GLL-YOLO method based on the YOLOv8 network is proposed to deal with problems such as fruit occlusion and complex backgrounds in mature blueberry detection. This approach utilizes the GhostNetV2 network as the backbone. The LIMC module is suggested to substitute the original C2f module. Meanwhile, a Lightweight Shared Convolution Detection Head (LSCD) module is designed to build the GLL-YOLO model. This model can accurately detect blueberries at three different maturity stages: unripe, semi-ripe, and ripe. It significantly reduces the number of model parameters and floating-point operations while maintaining high accuracy. Experimental results show that GLL-YOLO outperforms the original YOLOv8 model in terms of accuracy, with mAP improvements of 4.29%, 1.67%, and 1.39% for unripe, semi-ripe, and ripe blueberries, reaching 94.51%, 91.72%, and 93.32%, respectively. Compared to the original model, GLL-YOLO improved the accuracy, recall rate, and mAP by 2.3%, 5.9%, and 1%, respectively. Meanwhile, GLL-YOLO reduces parameters, FLOPs, and model size by 50%, 39%, and 46.7%, respectively, while maintaining accuracy. This method has the advantages of a small model size, high accuracy, and good detection performance, providing reliable support for intelligent blueberry harvesting. Full article

Phenology prediction is critical for optimizing the timing of rice crop management operations such as fertilization and irrigation, particularly in the face of increasing climate variability. This study aimed to estimate three key developmental stages in the temperate irrigated rice systems of Australia: panicle initiation (PI), flowering, and harvest maturity. Extensive and diverse field observations ($n\approx 302$) were collected over four consecutive seasons (2022–2025) from the rice-growing regions of the Murrumbidgee and Murray Valleys in southern New South Wales, encompassing six varieties and three sowing methods. The extent of data available allowed a number of traditional and emerging machine learning (ML) models to be directly compared to determine the most robust strategies to predict Australian rice crop phenology. Among all models, Tabular Prior-data Fitted Network (TabPFN), a pre-trained transformer model trained on large synthetic datasets, achieved the highest precision for PI and flowering predictions, with root mean square errors (RMSEs) of 4.9 and 6.5 days, respectively. Meanwhile, long short-term memory (LSTM) excelled in predicting harvest maturity with an RMSE of 5.9 days. Notably, TabPFN achieved strong results without the need for hyperparameter tuning, consistently outperforming other ML approaches. Across all stages, models that integrated remote sensing (RS) and weather variables consistently outperformed those relying on single-source input. These findings underscore the value of hybrid data fusion and modern time series modeling techniques for accurate and scalable phenology prediction, ultimately enabling more informed and adaptive agronomic decision-making. Full article

Berries are popular by consumers for improving vision, lowering blood sugar, improving circulation, and cardiovascular protection. They are usually small, thin-skinned, and fragile, with inconsistent ripening times. Harvesting robots are able to accurately determine the ripeness of fruits, avoiding pulp breakage and nutrient loss caused by manual squeezing. This work reviews the development and application of berry harvesting robots with market prospects in recent years. Next, this paper discusses the key technologies of berry picking robots, including fruit detection and localization technology, motion planning technology, and end-effector and harvesting mechanism. It also discusses the challenges currently faced in the development of berry harvesting robots, including external factors such as unstructured working environments and internal technical difficulties such as robot design and control. To address these challenges, future berry picking robots should focus on developing weak supervision recognition models based on deep learning, high-speed collision-free multi-arm collaborative harvesting technology, and high fault-tolerant harvesting technology to improve picking efficiency and quality, reduce fruit damage, and promote the automation and intelligence of the berry harvesting. Full article

To achieve efficient vineyard grape picking, a vision-based information processing framework integrating two-stage segmentation with morphological perception is proposed. In the first stage, an improved YOLOv8s-seg model is employed for coarse segmentation, incorporating two key enhancements: first, a dynamic deformation feature aggregation module (DDFAM), which facilitates the extraction of complex structural and morphological features; and second, an efficient asymmetric decoupled head (EADHead), which improves boundary awareness while reducing parameter redundancy. Compared with mainstream segmentation models, the improved model achieves superior performance, attaining the highest mAP@0.5 of 86.75%, a lightweight structure with 10.34 M parameters, and a real-time inference speed of 10.02 ms per image. In the second stage, the fine segmentation of fruit stems is performed using an improved OTSU thresholding algorithm, which is applied to a single-channel image derived from the hue component of the HSV color space, thereby enhancing robustness under complex lighting conditions. Morphological features extracted from the preprocessed fruit stem, including centroid coordinates and a skeleton constructed via medial axis transform (MAT), are further utilized to establish the spatial relationships with a picking point and cutting axis. The visualization analysis confirms the high feasibility and adaptability of the proposed framework, providing essential technical support for the automation of grape harvesting. Full article

Agriculture is facing increasing challenges due to labor shortages, rising productivity demands, and the need to operate in unstructured environments. Robotics, particularly soft robotics, offers promising solutions for automating delicate tasks such as fruit harvesting. While numerous soft grippers have been proposed, most focus on grasping and lack the capability to detach fruits with rigid peduncles, which require cutting. This paper presents a novel modular hexagonal soft gripper that integrates soft pneumatic actuators, embedded mechano-optical force sensors for real-time contact monitoring, and a self-centering iris-type cutting mechanism. The entire system is 3D-printed, enabling low-cost fabrication and rapid customization. Experimental validation demonstrates successful harvesting of bell peppers and identifies cutting limitations in tougher crops such as aubergine, primarily due to material constraints in the actuation system. This dual-capability design contributes to the development of multifunctional robotic harvesters capable of adapting to a wide range of fruit types with minimal requirements for perception and mechanical reconfiguration. Full article

Zanthoxylum armatum DC. (Z. armatum) is a versatile plant species valued for its aroma oil and nutritional components. However, the variability of chemical composition in Z. armatum fruits in the field remains largely unknown, and it is still unclear how harvest parameters affect the aroma and nutritional quality of the fruits. To address this gap, Z. armatum fruits from varying harvest times, tree ages, and fruiting positions were analyzed for physicochemical properties, nutrients, minerals, aroma profiles, and antioxidant activity. A quality assessment method was developed based on key Z. armatum fruit parameters. Results showed significant differences in the size, weight, total phenol, flavonoid and sanshool content of Z. armatum fruit from different harvest parameters. Z. armatum fruits contained abundant minerals, showing diverse harvest-condition variations. In vitro antioxidant assays showed higher ABTS/DPPH scavenging activity and reducing capacity (23–54 mg/g). HS-SPME-GC-MS identified 64 aroma compounds, encompassing terpenes, alcohols, etc. Linalool was the predominant constituent (46.65%). PLS-DA and Volcano plot analyses highlighted significant differences in VOCs among harvest times and tree ages, while fruit positions showed minimal impact. The Mantel test identified aroma-active compounds associated with antioxidant activity. These findings facilitate a science-based harvesting strategy to standardize Z. armatum fruit quality and marketability. Full article

Watermelon is a popular fruit, predominantly cultivated in Asian countries. However, the production and harvesting processes present several challenges. Due to its size and weight, manually harvesting watermelons is labor-intensive and costly. In the future, technology is expected to enable robots to harvest watermelons. Therefore, it becomes essential to introduce intelligent systems to effectively identify and locate watermelons in harvesting. This research aims to develop an advanced methodology for watermelon identification and location using You Look Only Once (YOLO)v8 and YOLOv8-oriented bounding box (OBB) algorithms. Furthermore, the simple online and real-time tracking (SORT) algorithm was employed to track and count watermelons and estimate yield. The performance of YOLOv8-OBB was better than that of YOLOv8 and the highest precision (0.938) was achieved by YOLOv8s-OBB. Additionally, the size of each watermelon was measured with both models. The models help farmers find the optimal watermelons for harvest. Full article

The low quality of mechanized tea harvesting in China’s hilly plantations, often caused by irregular canopy morphology, necessitates improved technology. This study addresses this issue by proposing a contact-based profiling mechanism and a corresponding control method for tea cutting platforms. This cutting platform mainly consists of a canopy profiling mechanism, a tea harvesting unit, a lifting actuator, and a control system, containing a mathematical model correlating the tea canopy pose with sensor signals. Following a theoretical analysis of key components of the profiling device, we determined their structural parameters. Subsequently, a profiling control strategy was formulated, and an automatic control system for the profiling cutting platform was developed. Finally, a prototype was constructed and subjected to experimental validation to assess the dynamic characteristics of its pose adjustment and its profiling-based harvesting performance. The results of this experiment illustrate that after implementing the profiling system, the proportion of time the cutting blade remained in an optimal cutting position increased from 26.5% to 95.0%, an improvement of 68.5%, demonstrating that the system successfully achieves its design objective of the adaptive profiling apparatus in response to variation in canopy morphology. In addition, the integrity rate of harvested tea leaves increased from 50.7% without profiling to 74.6% with profiling, an improvement of 47.1%, which indicates the good performance of this profiling cutting platform. Therefore, this research provides a valuable reference for the design of intelligent tea harvesting machinery for the hilly tea plantations in China. Full article

Contributing to the quest for renewable energy harvesting, we present, in the work at hand, a conceptual model of a large-scale wireless microwave power harvester that takes the structure of a smart reconfigurable harvesting surface. This structure is assembled by numerous elementary harvesters that, as a whole, present both wide solid angle coverage and high receiving antenna gain. This is achieved by employing two levels of organization, both in the horizontal and in the vertical planes. The horizontal plane, which is the host receiving surface, is tiled by employing square radiators and forms hierarchical subarray structures. At the same time, hieratical structures are also employed in the vertical plane where the beamforming network collects the received power in a drainage-basin fashion (one receiving port is fed by its assigned and also its neighboring antenna elements) achieving, in this way, increased efficiency. The presented results verify the contributed design. Full article

This study presents a machine learning-based framework for predicting the electrical output of a vibration energy harvesting system (VEHS) integrated with a Jeffcott rotor model. Vibration induced by rotor imbalance is converted into electrical energy via piezoelectric elements, and the system’s dynamic response is simulated using the fourth-order Runge–Kutta method across varying mass ratios, rotational speeds, and eccentricities. The resulting dataset is validated experimentally with a root-mean-square error below 5%. Three predictive models—Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and eXtreme Gradient Boosting (XGBoost)—are trained and evaluated. While DNN and LSTM yield a high predictive accuracy (R² > 0.9999), XGBoost achieves comparable accuracy (R² = 0.9994) with significantly lower computational overhead. The results demonstrate that among the tested models, XGBoost provides the best trade-off between speed and accuracy, achieving R² > 0.999 while requiring the least training time. These results demonstrate that XGBoost might be particularly suitable for real-time evaluation and edge deployment in rotor-based VEHS, offering a practical balance between speed and precision. Full article