Search Results (445)

Fruit tree image segmentation is an essential problem in automating a variety of agricultural tasks such as phenotyping, harvesting, spraying, and pruning. Many research papers have proposed a diverse spectrum of solutions suitable for specific tasks and environments. The review scope of this paper is confined to the front views of fruit trees, and 207 relevant papers proposing tree image segmentation in an orchard environment are collected using a newly designed crawling review method. These papers are systematically reviewed based on a four-tier taxonomy that sequentially considers the method, image, task, and fruit. This taxonomy will assist readers to intuitively grasp the big picture of these research activities. Our review reveals that the most noticeable deficiency of the previous studies was the lack of a versatile dataset and segmentation model that could be applied to a variety of tasks and environments. Six important future research topics, such as building large-scale datasets and constructing foundation models, are suggested, with the expectation that these will pave the way to building a versatile tree segmentation module. Full article

Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in this study, we evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation in two contrasting systems: pecan, a perennial nut crop, and onion, an annual vegetable. Ground-truth measurements included pecan tree number, tree height, and canopy area, as well as onion bulb number and diameter, the latter used for market class classification. Counting performance was assessed using precision, recall, and F1 score, while trait estimation was evaluated with linear regression analysis. UAV-based counts showed strong agreement with ground-truth data, achieving precision, recall, and F1 scores above 97% for both crops. For pecans, UAV-derived estimates of tree height (R² = 0.98, error = 11.48%) and canopy area (R² = 0.99, error = 23.16%) demonstrated high accuracy, while errors were larger in young trees compared with mature trees. For onions, UAV-derived bulb diameters achieved an R² of 0.78 with a 6.29% error, and market class classification (medium, jumbo, colossal) was predicted with <10% error. These findings demonstrate that UAV imagery integrated with a user-friendly AI platform can deliver accurate, scalable solutions for biometric monitoring in both perennial and annual specialty crops, supporting applications in harvest planning, orchard management, and market supply forecasting. Full article

When energy harvesting is not feasible or fails to provide sufficient power, the energy buffer of battery-powered Internet of Things (IoT) devices inevitably depletes. The proper disposal and/or replacement of depleted and end-of-life (EoL) batteries is challenging, especially in rural IoT deployments, where human intervention is cumbersome. When batteries are left in nature, they can pose a significant environmental risk, leaking harmful chemicals into the soil. This work proposes a novel contactless battery solution for longevity and recyclability, providing automated battery replacement using a short-range wireless power transfer (WPT) link instead of a direct battery-to-IoT node contact-based connection for powering the IoT device. It facilitates battery recovery at EoL by, e.g., an unmanned vehicle (UV), reducing the need for manual intervention. Unlike complex mechanical solutions or contacts prone to corrosion, a contactless approach enables easy replacement and improves reliability and longevity in harsh environments. A technical challenge is the need for an efficient contactless solution to enable the IoT node to get energy from the battery. This work elaborates an efficient wireless connection between the battery and IoT node, which ensures robustness in harsh environments. In addition, it examines the sustainability aspects of this approach. The WPT system is applied in two IoT node applications: polling-based and interrupt-based systems. The proposed solution achieves a transmitter-to-receiver efficiency of 72% and has an additional environmental impact of 2.34 kgCO2eq. However, its key advantage is the ease of battery replacement, which could significantly reduce the expected long-term environmental impact. Full article

The use of UAVs to detect litchi in natural environments is imperative for rapid litchi yield estimation and automated harvesting systems. However, UAV-based lychee fruit detection bottlenecks arise from complex canopy architecture and leaf occlusion. This study proposed a collaborative optimization strategy integrating agronomic technique with deep learning. Three leaf thinning intensities (0, 6, and 12 compound leaves) were applied at the early stage of fruit to systematically evaluate their effects on fruit growth, canopy structure, and detection performance. Results indicated that moderate defoliation (six leaves) significantly enhanced canopy openness and light penetration without adversely impacting on yield and fruit quality. Subsequent UAV-based detection under moderate versus no defoliation treatment revealed that the YOLOv8-based model achieved significant performance gains: mean average precision (mAP) increased from 0.818 to 0.884, and the F1-score improved from 0.796 to 0.842. The study contributes a novel collaborative optimization strategy that effectively mitigates occlusion issues in fruit detection. This approach demonstrates that agronomic techniques can be strategically used to enhance AI perception, offering a significant step forward in the integration of agricultural machinery and agronomy for intelligent orchard systems. Full article

Multi-agent coordination with task allocation, routing, and scheduling presents critical challenges when deploying heterogeneous robotic systems in constrained agricultural environments. These systems involve real-time sensing during their operations with various sensors, and having quick updates on coordination based on sensed data is critical. This paper addresses the specific requirements of harvesting automation through three heuristic approaches: (1) primal–dual workload balancing inspired by combinatorial optimization techniques, (2) greedy task assignment with iterative local optimization, and (3) LLM-based constraint processing through prompt engineering. Our agricultural application scenario incorporates robot size constraints for navigating narrow crop rows while optimizing task completion time. The greedy heuristic employs rapid initial task allocation based on proximity and capability matching, followed by iterative route refinement. The primal–dual approach adapts combinatorial optimization principles from recent multi-depot routing solutions, dynamically redistributing workloads between robots through dual variable adjustments to minimize maximum completion time. The LLM-based method utilizes structured prompt engineering to encode spatial constraints and robot capabilities, generating feasible solutions through successive refinement cycles. We implemented and compared these approaches through extensive simulations. Preliminary results demonstrate that all three approaches produce feasible solutions with reasonable quality. The results demonstrate the potential of the methods for real-world applications that can be quickly adopted into variations of the problem to offer valuable insights into solving complex coordination problems with heterogeneous multi-robot systems. Full article

Accurate real-time detection of blueberry maturity is vital for automated harvesting. However, existing methods often fail under occlusion, variable lighting, and dense fruit distribution, leading to reduced accuracy and efficiency. To address these challenges, we designed a lightweight deep learning framework that integrates improved feature extraction, attention-based fusion, and progressive transfer learning to enhance robustness and adaptability To overcome these challenges, we propose BMDNet-YOLO, a lightweight model based on an enhanced YOLOv8n. The backbone incorporates a FasterPW module with parallel convolution and point-wise weighting to improve feature extraction efficiency and robustness. A coordinate attention (CA) mechanism in the neck enhances spatial-channel feature selection, while adaptive weighted concatenation ensures efficient multi-scale fusion. The detection head employs a heterogeneous lightweight structure combining group and depthwise separable convolutions to minimize parameter redundancy and boost inference speed. Additionally, a three-stage transfer learning framework (source-domain pretraining, cross-domain adaptation, and target-domain fine-tuning) improves generalization. Experiments on 8250 field-collected and augmented images show BMDNet-YOLO achieves 95.6% mAP@0.5, 98.27% precision, and 94.36% recall, surpassing existing baselines. This work offers a robust solution for deploying automated blueberry harvesting systems. Full article

The accurate assessment of Chinese bayberry (Myrica rubra) maturity is critical for intelligent harvesting. This study proposes a novel cascaded framework combining instance segmentation and multi-feature regression for accurate maturity detection. First, a lightweight SOLOv2-Light network is employed to segment each fruit individually, which significantly reduces computational costs with only a marginal drop in accuracy. Then, a multi-feature extraction network is developed to fuse deep semantic, color (LAB space), and multi-scale texture features, enhanced by a channel attention mechanism for adaptive weighting. The maturity ground truth is defined using the a*/b* ratio measured by a colorimeter, which correlates strongly with anthocyanin accumulation and visual ripeness. Experimental results demonstrated that the proposed method achieves a mask mAP of 0.788 on the instance segmentation task, outperforming Mask R-CNN and YOLACT. For maturity prediction, a mean absolute error of 3.946% is attained, which is a significant improvement over the baseline. When the data are discretized into three maturity categories, the overall accuracy reaches 95.51%, surpassing YOLOX-s and Faster R-CNN by a considerable margin while reducing processing time by approximately 46%. The modular design facilitates easy adaptation to new varieties. This research provides a robust and efficient solution for in-field bayberry maturity detection, offering substantial value for the development of automated harvesting systems. Full article

Precise oriented object detection of corn provides critical support for automated agricultural tasks such as harvesting, spraying, and precision management. In this work, we address this challenge by leveraging oriented object detection in combination with depth information to estimate corn poses. To enhance detection accuracy while maintaining computational efficiency, we construct a precise annotated oriented corn detection dataset and propose YOLOv11OC, an improved detector. YOLOv11OC integrates three key components: Angle-aware Attention Module for angle encoding and orientation perception, Cross-Layer Fusion Network for multi-scale feature fusion, and GSConv Inception Network for efficient multi-scale representation. Together, these modules enable accurate oriented detection while reducing model complexity. Experimental results show that YOLOv11OC achieves 97.6% mAP@0.75, exceeding YOLOv11 by 3.2%, and improves mAP_50:95 by 5.0%. Furthermore, when combined with depth maps, the system achieves 92.5% pose estimation accuracy, demonstrating its potential to advance intelligent and automated cultivation and spraying. Full article

This study addresses the core challenges of precise marigold corollas recognition and efficient continuous path planning under complex natural conditions (strong illumination, occlusion, adhesion) by proposing an integrated lightweight visual recognition and real-time path planning framework. We introduce MPD-YOLO, an optimized model based on YOLOv11n, incorporating (1) a Multi-scale Information Enhancement Module (MSEE) to boost feature extraction; (2) structured pruning for significant model compression (final size: 2.1 MB, 39.6% of original); and (3) knowledge distillation to recover accuracy loss post-pruning. The resulting model achieves high precision (P: 89.8%, mAP@0.5: 95.1%) with reduced computational load (3.2 GFLOPs) while demonstrating enhanced robustness in challenging scenarios—recall significantly increased by 6.8% versus YOLOv11n. Leveraging these recognition outputs, an adaptive ant colony algorithm featuring dynamic parameter adjustment and an improved pheromone strategy reduces average path planning time to 2.2 s—a 68.6% speedup over benchmark methods. This integrated approach significantly enhances perception accuracy and operational efficiency for automated marigold harvesting in unstructured environments, providing robust technical support for continuous automated operations. Full article

Underwater wireless communication (UWC) is an emerging technology crucial for automating marine industries, such as offshore aquaculture and energy production, and military applications. It is a key part of the 6G vision of creating a hyperconnected world for extending connectivity to the underwater environment. Of the three main practicable UWC technologies (acoustic, optical, and radiofrequency), acoustic methods are best for far-reaching links, while optical is best for high-bandwidth communication. Recently, utilizing reconfigurable intelligent surfaces (RISs) has become a hot topic in terrestrial applications, underscoring significant benefits for extending coverage, providing connectivity to blind spots, wireless power transmission, and more. However, the potential for further research works in underwater RIS is vast. Here, for the first time, we conduct an extensive survey of state-of-the-art of RIS and metasurfaces with a focus on underwater applications. Within a holistic perspective, this survey systematically evaluates acoustic, optical, and hybrid RIS, showing that environment-aware channel switching and joint communication architectures could deliver holistic gains over single-domain RIS in the distance–bandwidth trade-off, congestion mitigation, security, and energy efficiency. Additional focus is placed on the current challenges from research and realization perspectives. We discuss recent advances and suggest design considerations for coupling hybrid RIS with optical energy and piezoelectric acoustic energy harvesting, which along with distributed relaying, could realize self-sustainable underwater networks that are highly reliable, long-range, and high throughput. The most impactful future directions seem to be in applying RIS for enhancing underwater links in inhomogeneous environments and overcoming time-varying effects, realizing RIS hardware suitable for the underwater conditions, and achieving simultaneous transmission and reflection (STAR-RIS), and, particularly, in optical links—integrating the latest developments in metasurfaces. Full article

Handling delicate agricultural products, such as tomatoes, requires careful attention from workers during harvesting, sorting, and packaging processes. This labor-intensive approach is often inefficient and susceptible to human error. A potential solution to improve efficiency is the development of automated systems capable of replacing manual labor. However, such systems face significant challenges due to the irregular shapes and fragility of these products, requiring specialized adaptable and soft gripping mechanisms. In this context, this paper introduces a parametric design methodology for 3D-printed flexible fingers in soft grippers, tailored for agricultural applications. The approach was tested in a case study that targeted soft agricultural products with diameters between 45 and 75 mm. Three finger topologies were modeled and compared to identify an optimal configuration. A prototype was then developed using 3D printing with Z-SemiFlex. Experimental tests confirmed that the prototype could grasp different fruits reliably and without surface damage. It achieved an Average Precision (AP) of 87.5% for tomatoes and 92.5% for mandarins across 80 trials. These results validate the feasibility of the proposed design methodology for fingers in soft grippers. Full article

Freshness is a critical indicator of fruit and vegetable quality, directly affecting nutrition, taste, safety, and reducing waste across supply chains. Accurate detection is essential for quality control, supporting producers during harvesting and storage, and guiding consumers in purchasing decisions. Traditional manual assessment methods remain subjective, labor-intensive, and susceptible to inconsistencies, highlighting the need for automated, efficient, and scalable solutions, such as the use of imaging sensors and Artificial Intelligence (AI). In this study, the efficacy of the Noisy Vision Transformer (NoisyViT) model was evaluated for fruit and vegetable freshness detection from images. Across five publicly available datasets, the model achieved accuracies exceeding 97% (99.85%, 97.98%, 99.01%, 99.77%, and 98.96%). To enhance generalization, these five datasets were merged into a unified dataset encompassing 44 classes of 22 distinct fruit and vegetable types, named Freshness44. The NoisyViT architecture was further expanded into a multi-task configuration featuring two parallel classification heads: one for freshness detection (binary classification) and the other for fruit and vegetable type classification (22-class classification). The multi-task NoisyViT model, fine-tuned on the Freshness44 dataset, attained outstanding accuracies of 99.60% for freshness detection and 99.86% for type classification, surpassing the single-head NoisyViT model (99.59% accuracy), conventional machine learning and CNN-based state-of-the-art methodologies. In practical terms, such a system can be deployed across supply chains, retail settings, or consumer applications to enable real-time, automated monitoring of fruit and vegetable quality. Overall, the findings underscore the effectiveness of the proposed multi-task NoisyViT model combined with the Freshness44 dataset, presenting a robust and scalable solution for the assessment of fruit and vegetable freshness. Full article

Accurate detection of tea shoots in natural environments is crucial for facilitating intelligent tea picking, field management, and automated harvesting. However, the detection performance of existing methods in complex scenes remains limited due to factors such as the small size, high density, severe overlap, and the similarity in color between tea shoots and the background. Consequently, this paper proposes an improved target detection algorithm, RFA-YOLOv8, based on YOLOv8, which aims to enhance the detection accuracy and robustness of tea shoots in natural environments. First, a self-constructed dataset containing images of tea shoots under various lighting conditions is created for model training and evaluation. Second, the multi-scale feature extraction capability of the model is enhanced by introducing RFCAConv along with the optimized SPPFCSPC module, while the spatial perception ability is improved by integrating the RFAConv module. Finally, the EIoU loss function is employed instead of CIoU to optimize the accuracy of the bounding box positioning. The experimental results demonstrate that the improved model achieves 84.1% and 58.7% in mAP@0.5 and mAP@0.5:0.95, respectively, which represent increases of 3.6% and 5.5% over the original YOLOv8. Robustness is evaluated under strong, moderate, and dim lighting conditions, yielding improvements of 6.3% and 7.1%. In dim lighting, mAP@0.5 and mAP@0.5:0.95 improve by 6.3% and 7.1%, respectively. The findings of this research provide an effective solution for the high-precision detection of tea shoots in complex lighting environments and offer theoretical and technical support for the development of smart tea gardens and automated picking. Full article

Understanding physiological variability in wild ungulates is essential for ecological monitoring and sustainable wildlife management. This study aimed to examine whether sex and season (autumn vs. early winter) significantly influence hematological and biochemical parameters in free-ranging red deer (Cervus elaphus) from the Eastern Carpathians, Romania. A total of 40 legally harvested adult individuals (20 males, 20 females) were included, and blood samples were collected post-mortem under standardized conditions to minimize pre-analytical variability. Hematological parameters (WBC, RBC, HGB, HCT, PLTs) and serum biochemical markers (glucose, urea, total cholesterol, triglycerides, total protein) were analyzed using automated veterinary analyzers. Statistically significant sex-related differences were found in hematocrit during autumn and hemoglobin concentration during winter, with higher values in males. Seasonal variation within sex groups was not significant but indicated a physiological trend toward hemoconcentration in winter. Biochemical values remained within reference ranges and showed no significant differences across groups. Pearson’s correlation analysis revealed a strong association between hematocrit and urea, and moderate correlations were observed between WBC and glucose, suggesting links between oxygen transport, protein metabolism, and energy balance. Environmental factors such as reduced food availability and temperature shifts during winter likely contribute to these physiological adjustments. These results provide baseline data for the physiological assessment of red deer populations and support the development of ecological health indicators in wildlife monitoring programs. Future studies incorporating hormonal and immunological biomarkers across multiple seasons are encouraged to further understand adaptive responses in cervids. Full article

Cotton is the most important natural fiber crop worldwide, and its automated harvesting is essential for improving production efficiency and economic benefits. However, cotton boll detection faces challenges such as small target size, fine-grained category differences, and complex background interference. This study proposes RA-CottNet, a high-precision object detection model with both directional awareness and attention-guided capabilities, and develops an open-source dataset containing 4966 annotated images. Based on YOLOv11n, RA-CottNet incorporates ODConv and SPDConv to enhance directional and spatial representation, while integrating CoordAttention, an improved GAM, and LSKA to improve feature extraction. Experimental results showed that RA-CottNet achieves 93.683% $Precision$, 86.040% $Recall$, 93.496% $mAP50$, 72.857% $mAP95$, and 89.692% $F1$-$score$, maintaining stable performance under multi-scale and rotation perturbations. The proposed approach demonstrated high accuracy and real-time capability, making it suitable for deployment on agricultural edge devices and providing effective technical support for automated cotton boll harvesting and yield estimation. Full article