Smart agricultural technology最新文献_第3页

CSM-YOLO: An integrated lightweight framework for UAV-based detection and geospatial analysis of missing cotton seedlings CSM-YOLO：基于无人机的棉花缺失苗检测与地理空间分析集成轻量级框架

IF 5.7 Q1 AGRICULTURAL ENGINEERING

Smart agricultural technology

Pub Date : 2025-12-24 DOI: 10.1016/j.atech.2025.101751

Xu Wang , Nueraili Aierken , Xuelin Zhou , Zhixin Yao , Bo Yang , Yongke Li , Xiangping Feng

Accurate monitoring of cotton seedling establishment is essential for optimizing yield and field management in precision agriculture. Gaps caused by germination failure or uneven mechanical sowing can substantially reduce final yield and complicate subsequent field operations. This study aimed to develop a real-time, accurate, and scalable framework for monitoring missing cotton seedling establishment and quantifying missing-seedling gaps from UAV imagery. We propose CSM-YOLO, an end-to-end integrated framework comprising three core components: (i) a lightweight yet high-performance detection model (CSM-YOLO), in which the CA-StarNet backbone enables efficient feature encoding, the multi-scale C3k2-RAB module enhances fine-grained seedling representation, and the adaptive APT-TAL label assignment strategy improves feature–anchor matching accuracy, with the three components jointly achieving an optimal balance among accuracy, robustness, and inference efficiency; (ii) two complementary pixel-to-geographic coordinate mapping strategies, including a novel linear interpolation method and an enhanced collinearity equation method, each offering distinct advantages in terms of computational efficiency and georeferencing accuracy; and (iii) a regional grading and visualization module for missing-seedling rates that supports both quantitative assessment and spatial interpretation within a unified workflow. CSM-YOLO achieved a mean average precision ([email protected]) of 97.23%, with class-specific precisions of 97.87% for cotton seedlings and 96.89% for missing-seedling holes, significantly outperforming mainstream models (Faster R-CNN, SSD, YOLOv3-tiny, YOLOv5n, YOLOv8n, YOLOv10n, YOLOv11n, and YOLOv11s). The two coordinate-mapping strategies yielded mean localization errors of 0.155 m and 0.004 m, respectively, when validated against orthophoto reference points. The framework provides high-precision detection, efficient georeferencing, and intuitive visualizations of seedling missing patterns. It explores the feasibility of an integrated pipeline that links detection, localization, and visualization for UAV-based cotton field monitoring. By coupling these components into a coherent workflow, the proposed system enables high-throughput quality assessment, precise reseeding planning, and accurate identification of weed-infested zones, thereby offering a robust and scalable solution for data-driven decision-making in precision cotton production.

棉花育苗的准确监测是精准农业产量优化和田间管理的基础。发芽失败或机械播种不均匀造成的间隙会大大降低最终产量，并使后续的田间作业复杂化。本研究旨在开发一个实时、准确、可扩展的框架，用于监测无人机图像中的棉花缺苗建立和量化缺苗间隙。我们提出CSM-YOLO，这是一个端到端集成框架，包括三个核心组件：(1)轻量级高性能检测模型（cms - yolo）， CA-StarNet骨干网实现高效特征编码，多尺度C3k2-RAB模块增强细粒度幼苗表示，自适应APT-TAL标签分配策略提高特征锚点匹配精度，三者共同实现精度、鲁棒性和推理效率的最佳平衡；（ii）两种互补的像素到地理坐标映射策略，包括一种新的线性插值方法和一种增强的共线性方程方法，每种方法在计算效率和地理参考精度方面都具有明显的优势；（iii）缺失率的区域分级和可视化模块，支持统一工作流程中的定量评估和空间解释。CSM-YOLO的平均精密度（[email protected]）为97.23%，对棉花幼苗的分类精密度为97.87%，对缺失苗孔的分类精密度为96.89%，显著优于主流模型（Faster R-CNN、SSD、YOLOv3-tiny、YOLOv5n、YOLOv8n、YOLOv10n、YOLOv11n和YOLOv11s）。当针对正射影像参考点进行验证时，这两种坐标映射策略的平均定位误差分别为0.155 m和0.004 m。该框架提供了高精度的检测、高效的地理参考和直观的幼苗缺失模式可视化。探讨了基于无人机的棉田监测中检测、定位、可视化一体化管道的可行性。通过将这些组件耦合到一个连贯的工作流程中，所提出的系统可以实现高通量质量评估、精确的补种计划和准确的杂草出没区域识别，从而为精确棉花生产中的数据驱动决策提供强大且可扩展的解决方案。

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引用次数: 0

Detection method of broken grains and impurities in harvested soybeans using feature wavelength selection and MobileNetV4-Unet-SGCPNet hybrid network 基于特征波长选择和MobileNetV4-Unet-SGCPNet混合网络的收获大豆碎粒和杂质检测方法

IF 5.7 Q1 AGRICULTURAL ENGINEERING

Smart agricultural technology

Pub Date : 2025-12-24 DOI: 10.1016/j.atech.2025.101749

Chengqian Jin , Gong Cheng , Zhichang Chang , Man Chen , TengXiang Yang , Yinyan Shi , Xiaobin Gai

To address the challenges of hyperspectral data redundancy, small-target segmentation difficulty, and insufficient model real-time performance in high-precision online detection of foreign matter and kernel breakage in machine-harvested soybeans, this study proposes a collaborative detection method based on "feature wavelength optimization + MobileNetV4-Unet-SGCPNet hybrid network". First, 18 key feature bands were screened from 400 - 1000 nm hyperspectral data using successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), constructing a multi-source feature spectral image dataset. Subsequently, a MobileNetV4-Unet-SGCPNet hybrid network was designed, with a lightweight MobileNetV4 as the encoder, combined with the symmetric encoder-decoder structure of Unet and the spatial detail-guided context propagation module (SGCP) to achieve high-precision segmentation of broken grains, complete grains, and impurities. Finally, pixel-wise voting was employed to fuse multi-band feature information, enhancing the model’s generalization capability. The results demonstrate that: on the test set, the model achieves an average intersection - over - union of 89.69 % for soybean component recognition, with a mean precision average of 94.55 %, a mean precision of 93.63 %, a frame rate of 4.77 FPS, a parameter count of only 2.86 MB, and a computational load of 35.78 GFLOPs. Compared to mainstream models, this method reduces parameters by 97.2 % and computational cost by 97.8 %, while the average intersection - over - union drops by only 6.2 %, with a frame rate improvement of over 5 times, striking a significant balance between detection accuracy and real-time performance. Cross-variety and cross-device validations further confirm that the model effectively adapts to morphological and spectral variations across different soybean varieties, exhibiting strong generalization ability. This study provides a core algorithmic foundation for online monitoring systems of intelligent harvester operation quality, offering critical support for enhancing the commercial value of machine-harvested soybeans and advancing the intelligence level of agricultural machinery.

针对机采大豆异物和籽粒破损高精度在线检测中存在的高光谱数据冗余、小目标分割困难、模型实时性不足等问题，本研究提出了一种基于“特征波长优化+ MobileNetV4-Unet-SGCPNet混合网络”的协同检测方法。首先，利用逐次投影算法（SPA）和竞争自适应重加权采样（CARS）从400 ~ 1000 nm高光谱数据中筛选出18个关键特征波段，构建多源特征光谱图像数据集；随后，设计了MobileNetV4-Unet- sgcpnet混合网络，以轻量级的MobileNetV4作为编码器，结合Unet的对称编解码器结构和空间细节引导的上下文传播模块（SGCP），实现了对破碎粒、完整粒和杂质的高精度分割。最后，采用逐像素投票融合多波段特征信息，增强模型的泛化能力。结果表明：在测试集上，该模型对大豆成分的识别准确率达到89.69%，平均精度为94.55%，平均精度为93.63%，帧率为4.77 FPS，参数计数仅为2.86 MB，计算负荷为35.78 GFLOPs。与主流模型相比，该方法减少了97.2%的参数，减少了97.8%的计算成本，而平均交叉超并率仅下降了6.2%，帧率提高了5倍以上，在检测精度和实时性之间取得了很好的平衡。跨品种和跨装置验证进一步证实，该模型能有效适应不同大豆品种的形态和光谱变化，具有较强的泛化能力。本研究为智能收获机运行质量在线监测系统提供了核心算法基础，为提高机采大豆商业价值、提高农机智能化水平提供了关键支撑。

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引用次数: 0

UAV remote sensing for rice false smut detection via improved YOLOv12n 基于改进YOLOv12n的水稻伪黑穗病无人机遥感检测

IF 5.7 Q1 AGRICULTURAL ENGINEERING

Smart agricultural technology

Pub Date : 2025-12-24 DOI: 10.1016/j.atech.2025.101756

Haiyong Weng , Leilei Su , Yuanpeng Liu , Geqiang Lu , Xintong Cao , Jie Lin , Wenjie Xu , Hairong Luo , Zuxin Cheng , Dapeng Ye

Rice false smut (RFS) has become widespread in major rice-growing regions. It not only reduce rice production, but more importantly, it produces toxins on panicles—posing a significant threat to food safety. Rapidly and accurately detecting RFS plays a key role in ensuring rice production. This study proposes a lightweight detection model named HSFF-YOLO, which is based on an improved YOLOv12n and utilizes unmanned aerial vehicle (UAV) remote sensing images. To address the challenge of small target detection during model development, the original loss function and Neck structure were replaced with the Shape-NWD loss function and the High-level Selective and Feature Fusion (HSFF) multi-scale feature fusion structure, respectively. This replacement effectively enhances the model’s detection accuracy through semantic screening and multi-scale feature fusion. To further highlight the lesion information of RFS, the Convolutional Block Attention Module-Parallel (CBAM-P) was incorporated to emphasize semantic and spatial information. Additionally, the lightweight DySample (Upsampling by Dynamic Sampling) upsampling module was introduced to reduce the model’s computational resource overhead. The results showed that the HSFF-YOLO model achieved an average accuracy of 80.7 %, with parameters of 1.89 M, a Floating Point Operations (FLOPs) of 5.6 G, and a model size of 4.09 MB. Compared with YOLOv12n, it has decreased 25 % and 21 % of parameters and model size, respectively, while still presented relatively better mAP0.5 %, precision and recall, with approximate 3 %, 3.4 % and 2.4 % improvement. This study also conducted evaluations of the severity levels of RFS in the two regions. The results showed that RFS in the Longyan region was significantly more severe. This study indicated that UAV imaging coupled with HSFF-YOLO has excellent potential for RFS detection and completed the entire process from the rapid detection of RFS to disease severity assessment.

水稻稻曲病在我国主要水稻产区普遍存在。它不仅减少了水稻产量，更重要的是，它在稻穗上产生毒素——对食品安全构成重大威胁。快速、准确地检测RFS对保证水稻生产具有关键作用。本研究提出了一种基于改进的YOLOv12n并利用无人机遥感图像的轻型检测模型HSFF-YOLO。为了解决模型开发过程中小目标检测的难题，将原有的损失函数和颈部结构分别替换为Shape-NWD损失函数和HSFF多尺度特征融合结构。这种替换通过语义筛选和多尺度特征融合有效地提高了模型的检测精度。为了进一步突出RFS的病变信息，我们引入了卷积块注意模块并行（Convolutional Block Attention Module-Parallel, cbamp）来强调语义和空间信息。此外，还引入了轻量级的DySample（动态采样）上采样模块，以减少模型的计算资源开销。结果表明，HSFF-YOLO模型的平均准确率为80.7%，参数为1.89 M，浮点运算（FLOPs）为5.6 G，模型大小为4.09 MB。与YOLOv12n相比，HSFF-YOLO模型的参数和模型大小分别下降了25%和21%，而mAP0.5 %、精度和召回率仍相对较好，分别提高了约3%、3.4%和2.4%。本研究还对这两个地区的RFS严重程度进行了评估。结果表明，龙岩地区RFS更为严重。本研究表明，无人机成像与HSFF-YOLO结合具有很好的RFS检测潜力，完成了从RFS快速检测到疾病严重程度评估的全过程。

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引用次数: 0

Drivers, barriers and grower perspectives of innovation adoption in the UK controlled environment agriculture sector 英国受控环境农业部门创新采用的驱动因素、障碍和种植者观点

IF 5.7 Q1 AGRICULTURAL ENGINEERING

Smart agricultural technology

Pub Date : 2025-12-24 DOI: 10.1016/j.atech.2025.101748

Iona Y Huang , Andrew M Beacham , Laura H Vickers , Hairong Mu , Ourania Tremma , Elias Maritan , Magdalena Kaczorowska-Dolowy , James M Monaghan

Controlled environment agriculture (CEA), which comprises both greenhouse and vertical farming, is a multi-billion-pound global industry contributing significantly to fruit and vegetable production. CEA allows growers to produce crops reliably in otherwise unfavourable climatic conditions and permits extended growing seasons. CEA is ideally placed to exploit the technological and digital revolution in agriculture for precision control of crop growth conditions, health, quality and yield. Despite the value of the sector, there is a paucity of data regarding drivers and barriers, adoption rates and grower opinion of implementing advanced technologies in commercial CEA production. We combined a Quick Scoping Review of global CEA technology literature comprising 3679 primary studies with a Delphi study of UK CEA growers and technology providers (25 and 18 participants in rounds one and two, respectively) to determine CEA technology research focus, stakeholder priorities and concerns. Comparison of categorised technology types between these approaches was used to assess the degree of synergy between the two. This revealed a global CEA technology research focus on modelling/simulation, energy, lighting and sensors. Meanwhile, the most used technologies in UK CEA were environmental control, lighting and nutrient application. This illustrates a fair degree of connect between research focus and industry requirements. However, some grower priorities, including nutrition and robotics, were under-represented in the literature. UK grower behaviour remains optimistic regarding the opportunities technology such as alternative energy sources and automation can offer CEA, including reduced costs, improved efficiency and crop quality, extended supply season and mitigation of import risk. This must, however, be balanced with threats including operational and capital costs, which are viewed as the most significant barriers to technology innovation uptake.

可控环境农业（CEA）包括温室农业和垂直农业，是一个价值数十亿英镑的全球产业，对水果和蔬菜生产做出了重大贡献。CEA允许种植者在其他不利的气候条件下可靠地生产作物，并允许延长生长季节。CEA是利用农业技术和数字革命来精确控制作物生长条件、健康、质量和产量的理想选择。尽管该部门具有价值，但缺乏有关驱动因素和障碍、采用率和在商业CEA生产中实施先进技术的种植者意见的数据。我们结合了对全球CEA技术文献的快速范围审查，包括3679项主要研究，以及对英国CEA种植者和技术提供者（分别在第一轮和第二轮中有25名和18名参与者）的德尔福研究，以确定CEA技术研究的重点、利益相关者的优先事项和关注点。这些方法之间的分类技术类型的比较被用来评估两者之间的协同程度。这表明全球CEA技术研究的重点是建模/仿真、能源、照明和传感器。同时，英国CEA中应用最多的技术是环境控制、照明和养分施用。这说明了研究重点和行业需求之间有相当程度的联系。然而，一些种植者的优先事项，包括营养和机器人，在文献中没有得到充分的代表。英国种植者对替代能源和自动化等技术可以为CEA提供的机会仍然持乐观态度，包括降低成本、提高效率和作物质量、延长供应季节和减轻进口风险。然而，这必须与包括运营和资本成本在内的威胁相平衡，这些威胁被视为采用技术创新的最大障碍。

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引用次数: 0

Optimization and process analysis of sunflower oriented seeding device based on CFD-DEM 基于CFD-DEM的向日葵定向播种器优化与工艺分析

IF 5.7 Q1 AGRICULTURAL ENGINEERING

Smart agricultural technology

Pub Date : 2025-12-24 DOI: 10.1016/j.atech.2025.101744

Xuan Zhao, Anbin Zhang, Fei Liu, Hongbin Bai, Yuxing Ren, Wenxue Dong

Oriented seeding, as a precise sowing method, ensures the uniform distribution of seeds in the soil, improves germination rate and uniformity, reduces seed waste, optimizes the competition between seeds, and ultimately increases yield per unit area. This study, based on CFD-DEM coupled simulation technology, designs and optimizes a sunflower directional seed metering device, aiming to achieve precise oriented seeding of sunflower seeds. Through the CFD-DEM coupled simulation model, the interaction between the seed and airflow was simulated, and the movement characteristics of the seed were analyzed, along with the effects of key design parameters such as metering tube length, diameter, sowing angle, and inlet negative pressure on sowing performance. Single-factor experiments and rotational orthogonal tests were conducted to analyze the impact of these parameters on sunflower seed movement characteristics. The results of the study show that the optimal parameter combination is a metering tube length of 350 mm, a sowing angle of 30°, a metering tube diameter of 16.5 mm, and an inlet negative pressure of 1960 Pa. Under these conditions, the directional rate of the seed metering device is 84.12%, and the seed spacing coefficient of variation is 4.81%, demonstrating excellent performance. This study provides technical support for sunflower oriented seeding and offers valuable references for the design of seed metering devices for other similar crops.

定向播种作为一种精确的播种方式，保证了种子在土壤中的均匀分布，提高了发芽率和均匀性，减少了种子的浪费，优化了种子之间的竞争，最终提高了单位面积产量。本研究基于CFD-DEM耦合仿真技术，设计并优化了一种向日葵定向播种装置，以实现向日葵种子的定向精准播种。通过CFD-DEM耦合仿真模型，模拟种子与气流的相互作用，分析种子的运动特性，以及计量管长度、直径、播种角度、进口负压等关键设计参数对播种性能的影响。通过单因素试验和旋转正交试验，分析了这些参数对葵花籽运动特性的影响。研究结果表明，最优参数组合为计量管长度为350 mm，播种角为30°，计量管直径为16.5 mm，进口负压为1960 Pa。在此条件下，排种器的定向率为84.12%，排种间距变异系数为4.81%，表现出优异的性能。本研究为向日葵定向播种提供了技术支持，也为其他类似作物的排种装置设计提供了有价值的参考。

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引用次数: 0

Double deep Q-network for intelligent control and energy efficiency optimization of zonal ventilation in laying-hen houses 蛋鸡舍区域通风智能控制与能效优化的双深q网络

IF 5.7 Q1 AGRICULTURAL ENGINEERING

Smart agricultural technology

Pub Date : 2025-12-24 DOI: 10.1016/j.atech.2025.101753

Changzeng Hu , Lihua Li , Limin Huo , Yuchen Jia , Zongkui Xie , Yao Yu

Precise environmental control in laying hen houses is essential for animal welfare and production efficiency. Traditional ventilation strategies based on fixed temperature thresholds cause significant environmental fluctuations and high energy consumption due to frequent fan cycling. To address this, we propose a ventilation control strategy utilizing a Double Deep Q-Network (Double DQN) reinforcement learning algorithm. The system partitions the hen house into four equal-volume zones, each equipped with a positive-pressure fan unit. These units cooperate with a central negative-pressure fan set for precise temperature and humidity regulation. The strategy employs a composite state space integrating real-time environmental parameters (temperature, humidity) and fan operation status. A multi-dimensional action space defines the on/off combinations for the 16 commands governing the four positive-pressure fan units. A dual-objective reward function incorporates both environmental parameter deviation from setpoints and penalties for fan switching. Experimental results demonstrate that the Double DQN strategy significantly reduces the standard deviation of temperature and humidity across all zones compared to traditional threshold control, achieving closer proximity to the target setpoint (26 °C, 70 %). Furthermore, it reduces the daily energy consumption of the positive-pressure fan units by 10.35 % (103.63 kWh total). This strategy markedly enhances environmental control precision and stability while conserving energy, offering a novel intelligent solution for sustainable facility livestock environmental management.

蛋鸡舍环境的精确控制对动物福利和生产效率至关重要。传统的基于固定温度阈值的通风策略由于风机循环频繁，导致环境波动大，能耗高。为了解决这个问题，我们提出了一种利用双深度Q-Network （Double DQN）强化学习算法的通风控制策略。该系统将鸡舍分成四个等容积的区域，每个区域都配备了一个正压风扇单元。这些单位配合中央负压风扇设置精确的温度和湿度调节。该策略采用综合实时环境参数（温度、湿度）和风扇运行状态的复合状态空间。多维动作空间定义了控制四个正压风扇单元的16个指令的开/关组合。双目标奖励函数包含环境参数偏离设定值和风扇切换的惩罚。实验结果表明，与传统的阈值控制相比，双DQN策略显著降低了所有区域的温度和湿度的标准差，更接近目标设定值（26°C, 70%）。同时使正压风机机组日能耗降低10.35%（合计103.63 kWh）。该策略显著提高了环境控制的精度和稳定性，同时节约了能源，为可持续设施家畜环境管理提供了一种新颖的智能解决方案。

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引用次数: 0

PHDT-DETR: A lightweight end-to-end detector for on-device truss tomato detection in greenhouses PHDT-DETR：一种轻量级的端到端检测器，用于在温室中进行设备桁架番茄检测

IF 5.7 Q1 AGRICULTURAL ENGINEERING

Smart agricultural technology

Pub Date : 2025-12-22 DOI: 10.1016/j.atech.2025.101742

Nengwei Yang , Peng Ji , Sen Lin , Ya Xiong

Visual perception systems are essential for harvesting robots in smart agriculture, but deployment is often limited by computational constraints. For real-time truss tomato detection in complex greenhouses, existing models rarely deliver high accuracy, low latency, and lightweight design on resource-constrained edge devices, especially under variable illumination. We introduce PHDT-DETR, a lightweight, end-to-end detector optimized for edge deployment. Building on the RT-DETR baseline, PHDT-DETR integrates a CSP-PMSFA backbone for efficient multi-scale feature extraction, a CA-HSFPN neck that enhances feature fusion via Coordinate Attention, a DRBC3 block that enhances multi-scale feature representation through multi-branch re-parameterized convolutions while trimming redundant computation, a TS-IFI encoder that reduces attention complexity, and a joint NWD+Shape-IoU regression loss that provides overlap-independent, aspect-ratio–aware supervision for slender, irregular tomato skewers. We further apply Layer-Adaptive Magnitude-based Pruning (LAMP) for aggressive compression. Experiments show that the pruned model achieves 90.8% mAP50 while reducing the parameter count to 6.1 M and the computational cost to 17.4 GFLOPs. Deployed on an NVIDIA Jetson Orin Nano Super and compiled with TensorRT, the model runs at 66.0 FPS with a compact 15.5 MB footprint, outperforming mainstream YOLO models. These results demonstrate the feasibility of deploying high-precision, real-time, end-to-end object detectors on resource-constrained edge devices for robotic harvesting in greenhouses.

视觉感知系统对于智能农业中的收获机器人至关重要，但部署往往受到计算约束的限制。对于复杂温室中的实时桁架番茄检测，现有模型很少在资源受限的边缘设备上提供高精度、低延迟和轻量级设计，特别是在可变照明下。我们介绍了PHDT-DETR，这是一种轻量级的端到端检测器，针对边缘部署进行了优化。基于RT-DETR基线，PHDT-DETR集成了用于高效多尺度特征提取的CSP-PMSFA主干、通过坐标注意增强特征融合的CA-HSFPN颈部、通过多分支重新参数化卷积增强多尺度特征表示的DRBC3块、减少冗余计算的TS-IFI编码器、以及提供重叠无关的NWD+Shape-IoU联合回归损失。对细长、不规则的番茄串进行宽高比感知监督。我们进一步应用基于层自适应幅度的剪枝（LAMP）进行主动压缩。实验表明，修正后的模型mAP50达到90.8%，参数个数减少到6.1 M，计算成本减少到17.4 GFLOPs。该模型部署在NVIDIA Jetson Orin Nano Super上，并使用TensorRT进行编译，运行速度为66.0 FPS，占用空间为15.5 MB，优于主流的YOLO模型。这些结果证明了在资源受限的边缘设备上部署高精度、实时、端到端目标探测器用于温室机器人收获的可行性。

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引用次数: 0

FEM-Net:Feature-Enhanced Vision Mamba Network for camellia fruit segmentation FEM-Net：用于山茶果实分割的特征增强视觉曼巴网络

IF 5.7 Q1 AGRICULTURAL ENGINEERING

Smart agricultural technology

Pub Date : 2025-12-20 DOI: 10.1016/j.atech.2025.101738

Yang Li , Hongwei Deng , Songling Xia , Ming Yao , Wenli Fu

The natural growth conditions of camellia fruits are complex, and factors such as being obscured by branches and leaves, overlapping, and uneven light exposure can significantly affect the harvesting process, resulting in low efficiency. This paper focuses on the camellia fruit in natural settings and proposes a novel image segmentation algorithm for camellia fruits, called FEM-Net. First, we introduce the Mamba Attention Fusion block (MAF block) to enhance feature extraction, helping the model focus on the target areas and more accurately identify the morphological features of the camellia fruit. Additionally, we design the Global-Local Aggregation Module (GLA) to improve the model’s ability to perceive both local and global information, thus enhancing its capacity to capture the fine details of the camellia fruit. Experimental validation shows that FEM-Net achieves Precision, Recall, and F1 scores of 93.48 %, 95.76 %, and 94.52 %, respectively, on the camellia fruit dataset. Compared to the baseline model, the Precision, Recall, and F1 scores improved by 3.74 %, 3.60 %, and 3.85 %, respectively, outperforming other segmentation models.

山茶果实的自然生长条件复杂，枝叶遮挡、重叠、光照不均匀等因素会显著影响采收过程，导致采收效率低下。本文以自然环境下的山茶果实为研究对象，提出了一种新的山茶果实图像分割算法FEM-Net。首先，我们引入曼巴注意力融合块（Mamba Attention Fusion block, MAF block）增强特征提取，帮助模型聚焦目标区域，更准确地识别山茶果实的形态特征。此外，我们设计了全局-局部聚合模块（global - local Aggregation Module， GLA），提高了模型对局部和全局信息的感知能力，从而增强了模型对茶花果实精细细节的捕捉能力。实验验证表明，FEM-Net在山茶数据集上的准确率（Precision）、召回率（Recall）和F1得分分别为93.48%、95.76%和94.52%。与基线模型相比，Precision， Recall和F1分数分别提高了3.74%，3.60%和3.85%，优于其他分割模型。

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引用次数: 0

Crop pest classification using micro-doppler signatures via C-band radar in an anechoic chamber 利用消声室中c波段雷达的微多普勒特征对作物害虫进行分类

IF 5.7 Q1 AGRICULTURAL ENGINEERING

Smart agricultural technology

Pub Date : 2025-12-19 DOI: 10.1016/j.atech.2025.101736

Antonio L. Angulo Salas , Regina Hakvoort , Pedro Yamamoto , Hugo E. Hernandez-Figueroa

With the global population steadily rising, ensuring agricultural productivity requires more effective pest management strategies. Conventional detection methods, based on field scouting and visual inspection, often result in delayed responses and significant crop losses. This study introduces a novel approach that combines C-band Doppler radar with deep learning to detect and classify agricultural pests in cotton, citrus, and soybean crops. By capturing micro-Doppler spectral signatures generated by insect movements, radar provides distinctive features for species and life stage identification. Experiments were conducted in controlled conditions, where insects interacted with host plant leaves to simulate realistic field scenarios. Multiple convolutional neural network (CNN) architectures were evaluated, achieving validation accuracies between 91.68 % and 97.1 %, with a Macro AUC  ≥  0.992. These results confirm that radar signatures can reliably discriminate pest species and developmental stages. Beyond high accuracy, this method offers real-time, non-invasive monitoring, addressing limitations of traditional scouting techniques. The findings position radar sensing as a powerful tool for early pest detection and precision agriculture, enabling timely interventions that minimize yield losses. By integrating radar technology with advanced machine learning, this work contributes to more sustainable and resilient agricultural systems, supporting global food security through smarter pest management.

随着全球人口的稳步增长，确保农业生产力需要更有效的有害生物管理战略。基于田间侦察和目视检查的传统检测方法往往导致响应延迟和重大作物损失。本研究提出了一种将c波段多普勒雷达与深度学习相结合的新方法，用于棉花、柑橘和大豆作物的农业害虫检测和分类。通过捕获昆虫运动产生的微多普勒光谱特征，雷达为物种和生命阶段识别提供了独特的特征。实验在受控条件下进行，昆虫与寄主植物叶片相互作用，模拟真实的野外场景。对多个卷积神经网络（CNN）架构进行评估，验证准确率在91.68% ~ 97.1%之间，宏AUC ≥ 0.992。这些结果证实了雷达信号可以可靠地区分害虫的种类和发育阶段。除了高精度之外，该方法还提供实时、非侵入性监测，解决了传统侦察技术的局限性。研究结果表明，雷达传感是早期害虫检测和精准农业的有力工具，能够及时采取干预措施，最大限度地减少产量损失。通过将雷达技术与先进的机器学习相结合，这项工作有助于建立更具可持续性和抗灾能力的农业系统，通过更智能的病虫害管理支持全球粮食安全。

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引用次数: 0

Comparison of spray drift between spraying drone and conventional airblast sprayer in vineyards 无人机与传统喷风喷雾器在葡萄园喷雾漂移的比较

IF 5.7 Q1 AGRICULTURAL ENGINEERING

Smart agricultural technology

Pub Date : 2025-12-19 DOI: 10.1016/j.atech.2025.101741

Vasilis Psiroukis , Aikaterini Kasimati , Konstantinos Nychas , Evangelos Anastasiou , Athanasios Balafoutis , Spyros Fountas

Spraying Unmanned Aerial Vehicles (UAVs) are autonomous airborne platforms that primarily operate on predetermined flight plans and spraying missions. Although spraying UAVs are increasingly used for plant protection in vineyards, limited experimental evidence exists on how operational parameters influence spray drift under real field conditions, especially in European vineyards. This study quantified ground-level drift from a UAV sprayer in a commercial vineyard, evaluating two flight altitudes (2.0 m and 2.5 m AGL), two flight speeds (1.0 and 1.5 m/s), and three application strategies (inter-row with and without a buffer line, and over-row with a buffer line). An additional set of replicates using a conventional air-assisted sprayer was included as a reference for current vineyard practice. Spray drift was measured at multiple downwind distances using filter paper collectors and analysed with laboratory spectrophotometric methods following ISO 22,866. Drift from UAV applications was highly concentrated near the field boundary and declined sharply within the first 5 m for all configurations. Flight altitude was the dominant driver: increasing AGL from 2.0 m to 2.5 m raised drift at the closest sampling point by 30–70 %. Higher flight speed (1.5 m/s) increased drift by 10–20 % compared with 1.0 m/s. Applying a buffer reduced drift by up to 60 %, particularly in inter-row spraying. Under optimal UAV settings (2.0 m AGL, 1.0 m/s, buffer applied), drift became negligible beyond 10 m downwind. Compared with the conventional air-assisted sprayer, UAV applications under optimised conditions reduced drift at the closest sampling distance by approximately 65–70 % and showed substantially lower drift beyond 10 m. These findings demonstrate that appropriate UAV operational settings can significantly reduce off-target movement and offer a lower-drift alternative to conventional terrestrial sprayers in vineyard applications and such mitigation strategies should always be considered prior to designing a flight plan or spray mission.

喷洒无人机（uav）是一种自主的机载平台，主要执行预定的飞行计划和喷洒任务。尽管喷洒无人机越来越多地用于葡萄园的植物保护，但在实际现场条件下，特别是在欧洲葡萄园，操作参数如何影响喷雾漂移的实验证据有限。本研究量化了商业葡萄园中无人机喷雾器的地面漂移，评估了两种飞行高度（2.0 m和2.5 m AGL），两种飞行速度（1.0和1.5 m/s），以及三种应用策略（行间有和没有缓冲线，行上有缓冲线）。另外一组使用传统空气辅助喷雾器的重复试验也被包括在内，作为当前葡萄园实践的参考。使用滤纸收集器在多个顺风距离上测量喷雾漂移，并根据ISO 22,866使用实验室分光光度法进行分析。无人机应用的漂移高度集中在场边界附近，并且在所有配置的前5 m内急剧下降。飞行高度是主要驱动因素：从2.0 m到2.5 m的AGL增加使最近采样点的漂移增加30 - 70%。与1.0 m/s相比，更高的飞行速度（1.5 m/s）使漂移增加了10 - 20%。施用缓冲剂可减少高达60%的漂移，特别是在行间喷洒时。在最佳的无人机设置（2.0 m AGL, 1.0 m/s，使用缓冲）下，下风10 m以上的漂移可以忽略不计。与传统的空气辅助喷雾器相比，优化条件下的无人机应用在最近采样距离处减少了大约65 - 70%的漂移，并且在10米以上显示出明显较低的漂移。这些发现表明，适当的无人机操作设置可以显着减少偏离目标的移动，并为葡萄园应用中的传统地面喷雾器提供更低漂移的替代方案。在设计飞行计划或喷雾任务之前，应始终考虑此类缓解策略。

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引用次数: 0