[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3101":3,"related-3101":37},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":8,"created_at":36},3101,"AI 接管稻田：四川眉山永丰村 300 亩 AI 试点水稻亩产 826.8-863.6 公斤","https:\u002F\u002Fai-damn.com\u002Fai-takes-over-the-rice-fields-863-6-kg-per-mu-in-sichuan-pilot-1789513373662","9-14 四川省眉山市东坡区太和镇永丰村千亩高标准农田 300 亩 AI 试点田通过专家组测产验收：\"华浙优 210\"（高产优质杂交稻）亩产 826.8 公斤、\"胜两优 222\"（超高产籼粳杂交稻）亩产 863.6 公斤、\"全优 169\"（超高产杂交籼稻）亩产 858.8 公斤。AI 系统通过无人机巡检采集数据，对种植、水肥调控和病虫害早期预警提供精准建议。四川农业大学水稻栽培专家马均教授表示，结合良种、良法与 AI 精准管理可有效释放水稻增产潜力，为大规模单产提升提供可复制技术路径；今年永丰村共有 240 余个新品种在产量\u002F株型\u002F米质上表现良好，智能精准播种技术与 AI 应用已初步见效。",null,"## AI Takes Over the Rice Fields: 863.6 kg per Mu in Sichuan Pilot\n\nIn the rolling fields of Yongfeng Village, Tahe Town, Dongpo District, Meishan City, Sichuan Province, something unusual happened this harvest season. On September 14, as combines rolled through the thousand-mu high-standard farmland, 300 mu of it had been managed not by traditional farming wisdom alone, but by an **AI model** specifically designed for rice cultivation.\n\nGone are the days of \"judging fields by experience.\" Now, it's all about **making decisions based on data**.\n\n### From Experience to Data\n\nThe embankments were crowded with agricultural experts and curious farmers, all gathered to witness a field test. An expert group organized by the Sichuan Provincial Science and Technology Department was evaluating a project led by Sichuan Agricultural University: the \"Integrated Demonstration and Application of High-quality, High-yield, and Efficient Production Technologies for Rice-Vegetable (Medicinal) Crops in the Chengdu Plain.\"\n\nSo how does it work? The AI system collects data through **drone inspections**, then provides precise recommendations on planting, water and fertilizer regulation, and pest and disease early warning.\n\nLocal large-scale grain farmer Zhao Youyong put it simply: \"Before, farming relied on experience for field inspections. Now, using drones and the AI system, we get timely information about pests and diseases, so we can handle them directly. Farming has become more convenient.\"\n\n### The Numbers That Matter\n\nThe expert group's standardized yield test delivered solid results. All three core varieties in the 300-mu AI pilot fields performed impressively:\n\n*   **\"Huazheyous 210\"** (high-yield, high-quality hybrid rice): 826.8 kg per mu\n*   **\"Shengliangyou 222\"** (super-high-yield indica-japonica hybrid rice): **863.6 kg per mu**\n*   **\"Quanyou 169\"** (super-high-yield hybrid indica rice): 858.8 kg per mu\n\nMa Jun, a rice cultivation expert at Sichuan Agricultural University, explained that these yields prove that combining quality seeds with appropriate methods and AI precision management can **effectively release the potential for rice yield increase**. It offers a replicable technical path for large-scale yield improvement.\n\nHe also noted that more than 240 new varieties demonstrated good performance in yield, plant shape, and rice quality in Yongfeng Village this year. The application of intelligent precision sowing technology and AI has already shown initial results.\n\n### What This Means for the Future\n\nThis pilot isn't just about one good harvest. It's a glimpse into how **AI can transform traditional agriculture**. By moving from experience-based to data-driven farming, growers can make more informed decisions, reduce risks, and potentially achieve higher yields sustainably.\n\nAs Ma Jun pointed out, the combination of quality seeds, appropriate methods, and AI precision management provides a technical path that can be replicated on a larger scale. For a country that feeds 20% of the world's population with less than 10% of its arable land, such innovations are more than welcome—they're essential.\n\n### Key Points\n\n*   **AI-managed pilot field** in Sichuan achieved rice yields up to **863.6 kg per mu**.\n*   **Drones and data** replaced traditional experience-based farming for planting, fertilization, and pest control.\n*   **Three rice varieties** all exceeded 826 kg per mu, proving the effectiveness of AI precision management.\n*   **Experts say** this approach offers a replicable path for large-scale yield improvement.\n*   **The future of farming** is shifting from \"judging fields by experience\" to \"making decisions based on data.\"","AI DAMN","2026-09-14T10:00:00Z","报道",10,false,76,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,17,9,6,1,"AI精准管理水稻试点实测亩产数据具体、多方信源，具备可复制的智慧农业示范价值，值得入选每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","水稻","精准农业","无人機巡田",[32,33],"四川眉山 永丰村 AI水稻","四川农业大学 水稻 AI试点","四川眉山永丰村AI水稻-3101",0,"2026-09-22T00:05:33.996455Z",{"total":20,"page":21,"page_size":20,"items":38},[39,100,137,170,210,261],{"id":40,"title":41,"url":42,"summary":43,"summary_zh":44,"content":8,"source_name":45,"source_url":42,"published_at":46,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":48,"sources":53,"tags":55,"search_phrases":57,"slug":60,"view_count":35,"doi":61,"paper":62,"created_at":99},2512,"Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1698781","Accurate estimation of crop area using classification techniques applied to drone imagery plays an important role in precision agriculture. Traditional machine learning (ML) approaches have been widely used for agricultural image classification; however, advanced deep learning (DL) models generally provide superior feature extraction and classification capability for complex crop patterns. Differentiating various rice establishment methods is essential for precise area estimation. In this study, advanced deep learning models were employed to classify three types of rice cultivation: (i) Broadcasted Direct Seeded Rice (DSR), (ii) Wet Direct Seeded Rice (Wet DSR), and (iii) Transplanted Rice (TR) using drone imagery. The drone imagery was collected from an experimental field at Praanadhaara Organised Agro Forestry Private Limited, Bapatla District, Andhra Pradesh, India. The images were captured in the visible spectrum (Red, Green, and Blue bands) on three dates, viz., 15 October 2023, 27 October 2023, and 01 December 2023, from an altitude of 40 m and were used to train and test classification models. Classification was performed using six ResNet-50 based hybrid models, namely, ResNet-50+K-Nearest Neighbor (ResNet-50+KNN), ResNet-50+Support Vector Machine (ResNet-50+SVM), ResNet-50+Decision Trees (ResNet-50+DT), ResNet-50+Random Forest (ResNet-50+RF), ResNet-50+Naïve Bayes (ResNet-50+NB), and ResNet-50+Neural Network (ResNet-50+NN), along with two additional DL architectures, namely, You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8). Model performance was evaluated using overall accuracy (OA), precision (P), recall (R), kappa coefficient (K), F1-score (F1), and mean Average Precision (mAP). Among the tested classifiers, the ResNet-50+NN model consistently achieved the highest average overall accuracies of 93.16%, 95.81%, and 93.31% at T 1 , T 2 , and T 3 , respectively, outperforming all other models, whose accuracies ranged from 78.05% to 92.07%, 86.11%–95.24%, and 77.47%–92.09% across the respective time intervals. The ResNet-50+NN model also recorded the highest precision (0.93–0.97), recall (0.92–0.97), F1-score (0.92–0.97), and kappa coefficient (0.89–0.96), demonstrating superior and consistent classification performance across all observation dates. The methodology developed in this work enables precise identification of rice establishment methods, improving crop mapping and monitoring. This identification enhances resource efficiency, optimizes input use, and supports site-specific management, contributing to sustainable precision agriculture.","利用分类技术对无人机影像进行作物面积精确估算，在精准农业中发挥着重要作用。传统机器学习（ML）方法已广泛用于农业图像分类；然而，先进的深度学习（DL）模型通常对复杂作物模式具有更优越的特征提取和分类能力。区分不同的水稻种植方式对于精确估算面积至关重要。本研究采用先进的深度学习模型，利用无人机影像对三种水稻种植类型进行分类：（i）撒播直播稻（DSR），（ii）湿润直播稻（Wet DSR），以及（iii）移栽稻（TR）。无人机影像采集自印度安得拉邦巴帕特拉县Praanadhaara Organised Agro Forestry Private Limited的试验田。图像在可见光谱（红、绿、蓝波段）下于三个日期拍摄，即2023年10月15日、2023年10月27日和2023年12月1日，飞行高度为40 m，用于训练和测试分类模型。分类采用六种基于ResNet-50的混合模型，即ResNet-50+K近邻（ResNet-50+KNN）、ResNet-50+支持向量机（ResNet-50+SVM）、ResNet-50+决策树（ResNet-50+DT）、ResNet-50+随机森林（ResNet-50+RF）、ResNet-50+朴素贝叶斯（ResNet-50+NB）和ResNet-50+神经网络（ResNet-50+NN），以及两种额外的深度学习架构，即You Only Look Once第5版（YOLOv5）和You Only Look Once第8版（YOLOv8）。采用总体精度（OA）、精确率（P）、召回率（R）、Kappa系数（K）、F1分数（F1）和平均精度均值（mAP）评估模型性能。在测试的分类器中，ResNet-50+NN模型在T₁、T₂和T₃分别持续取得最高的平均总体精度，为93.16%、95.81%和93.31%，优于所有其他模型，后者的精度在相应时间段分别为78.05%–92.07%、86.11%–95.24%和77.47%–92.09%。ResNet-50+NN模型还记录了最高的精确率（0.93–0.97）、召回率（0.92–0.97）、F1分数（0.92–0.97）和Kappa系数（0.89–0.96），在所有观测日期均表现出优越且稳定的分类性能。本研究开发的方法能够精确识别水稻种植方式，改进作物制图和监测。这种识别增强了资源","Frontiers in Remote Sensing","2026-09-14T00:00:00Z","论文",{"impact":49,"substance":50,"depth":18,"authority":51,"freshness":19,"relevant":21,"comment":52},16,21,13,"基于无人机可见光影像与ResNet-50混合模型区分三类水稻种植方式，方法对比系统、精度数据扎实，对精准农业作物制图有参考价值，但属细分技术进展，影响范围有限。",[54],{"name":45,"url":42},[26,27,28,29,56],"遥感",[58,59],"农业人工智能 智慧农业 精准农业 水稻","农业人工智能 智慧农业","农业人工智能智慧农业精准农业水稻-2512","10.3389\u002Ffrsen.2026.1698781",{"doi":61,"openalex_id":63,"authors":64,"venue":45,"cited_by_count":35,"oa_url":90,"card":91,"direction":97,"ingested_from":98},"W7212834743",[65,67,69,71,73,76,78,80,82,85,87],{"name":66,"orcid":8},"Amrutha Lakshmi Gubbala",{"name":68,"orcid":8},"Santosha Rathod",{"name":70,"orcid":8},"Ramesh Dasyam",{"name":72,"orcid":8},"Mahender Kumar Rapolu",{"name":74,"orcid":75},"Arun Kumar Dasari","https:\u002F\u002Forcid.org\u002F0000-0001-7398-8601",{"name":77,"orcid":8},"Pundarikakshudu Kurra",{"name":79,"orcid":8},"Hanuma Raviteja Madireddy",{"name":81,"orcid":8},"Prajwal R. Shashishekhar",{"name":83,"orcid":84},"Ravi V. Mural","https:\u002F\u002Forcid.org\u002F0000-0002-5489-9918",{"name":86,"orcid":8},"Anil Kumar",{"name":88,"orcid":89},"R. M. Sundaram","https:\u002F\u002Forcid.org\u002F0000-0002-9857-8251","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1698781\u002Fpdf",{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"用无人机可见光影像和深度学习模型区分直播稻、湿直播稻与移栽稻三种水稻种植方式。","无人机RGB影像三个时相，比较ResNet-50混合模型与YOLOv5\u002Fv8分类","ResNet-50+NN表现最优，三期总体精度达93.16%、95.81%、93.31%，优于其他模","农业遥感与作物表型","可扩展到多光谱\u002F多时相与更大区域验证，并用于种植方式制图与面积精准估算。","智慧农业 \u002F 农业物联网","openalex","2026-09-15T23:30:08.600535Z",{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":8,"source_name":106,"source_url":103,"published_at":107,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":113,"tags":115,"search_phrases":118,"slug":121,"view_count":35,"doi":122,"paper":123,"created_at":136},3019,"PSPE-UNet: Projection-based Similarity Prototype Embedding UNet for Apple Leaf Disease Segmentation","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.18","Apple leaf disease segmentation plays a significant role in precision agriculture by enabling the accurate identification and localization of infected regions at the pixel level.However, diverse apple leaf diseases exhibit similar symptoms, such as overlapping lesions makes it challenging to distinguish between various disease classes.In this research, a Projection-based Similarity Prototype Embedding UNet (PSPE-UNet) is proposed to segment apple leaf diseases.Employing a projection head with a similarity prototype embedding in UNet enhances feature discrimination by mapping pixel-level representations into a normalized embedding space.This ensures better separation between healthy and disease regions, even when the regions exhibit similar texture and chromatic characteristics.Three learnable prototypes corresponding to healthy, disease, and boundary regions are used.The boundary prototype act as learnable auxiliary feature prototype within the auxiliary boundary branch to compute boundary probability map during training while disease prediction is based on healthy and disease prototypes.In addition, this method enhances the boundary delineation for irregular and small lesions by refining the feature alignment.Hence, the proposed PSPE-UNet achieves a high Pixel Accuracy (PA) of 98.96%, which is compared to existing methods such as the AS-DeepLabV3+ on the Apple Tree Leaf Disease Segmentation Dataset (ATLDSD).Moreover, proposed PSPE-UNet obtains an inference time of 0.0217s per batch (8 images), corresponding to 0.0027s per image on ATLDSD dataset compared to traditional methods like UNet.","苹果叶片病害分割在精准农业中具有重要意义，能够在像素级别上准确识别和定位感染区域。然而，不同苹果叶片病害表现出相似的症状，例如病灶重叠使得区分不同病害类别具有挑战性。本研究提出了一种基于投影的相似性原型嵌入UNet（PSPE-UNet）用于苹果叶片病害分割。在UNet中采用带有相似性原型嵌入的投影头，通过将像素级表示映射到归一化嵌入空间来增强特征判别能力。这确保了健康和病害区域之间更好的分离，即使这些区域表现出相似的纹理和色彩特征。使用三个可学习原型分别对应健康、病害和边界区域。边界原型在辅助边界分支中作为可学习辅助特征原型，在训练期间计算边界概率图，而病害预测则基于健康和病害原型。此外，该方法通过细化特征对齐增强了对不规则和小病灶的边界描绘。因此，所提出的PSPE-UNet在苹果树叶病害分割数据集（ATLDSD）上达到了98.96%的高像素精度（PA），并与现有方法如AS-DeepLabV3+进行了比较。此外，所提出的PSPE-UNet在ATLDSD数据集上获得了每批次（8张图像）0.0217秒的推理时间，相当于每张图像0.0027秒，与UNet等传统方法相比具有优势。","International journal of intelligent engineering and systems","2026-09-19T00:00:00Z",70,{"impact":110,"substance":111,"depth":18,"authority":110,"freshness":19,"relevant":21,"comment":112},12,20,"提出基于相似度原型嵌入的UNet分割方法，在苹果叶病害数据集上取得98.96%像素精度，方法新颖、数据明确，但属细分算法研究，产业影响有限。",[114],{"name":106,"url":103},[26,27,29,116,117],"图像分割","苹果病害",[119,120],"PSPE-UNet 苹果叶病害 分割","苹果叶病害 数据集 ATLDSD","PSPE-UNet苹果叶病害分割-3019","10.22266\u002Fijies2026.1031.18",{"doi":122,"openalex_id":124,"authors":125,"venue":106,"cited_by_count":35,"oa_url":103,"card":130,"direction":134,"ingested_from":98},"W7213634285",[126,128],{"name":127,"orcid":8},"Vedamurthy Hadavanahalli Kumaraiah",{"name":129,"orcid":8},"Shrinivasacharya Purohit",{"tldr":131,"method":132,"finding":133,"direction":134,"opportunity":135},"提出PSPE-UNet，用投影相似原型嵌入分割苹果叶片病害区域。","UNet+投影头+可学习原型（健康\u002F病害\u002F边界），ATLDSD数据集。","像素精度98.96%，单图推理0.0027秒，优于AS-DeepLabV3+和UNet。","农业人工智能与决策模型","可探索原型嵌入在相似症状多病害区分及轻量化边缘部署中的泛化能力。","2026-09-20T23:30:34.933307Z",{"id":138,"title":139,"url":140,"summary":141,"summary_zh":142,"content":8,"source_name":106,"source_url":140,"published_at":107,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":143,"score_detail":144,"sources":146,"tags":148,"search_phrases":150,"slug":153,"view_count":35,"doi":154,"paper":155,"created_at":169},3009,"AgriMAC: An Attention Based Multimodal Deep Clustering Framework for Rice Health Assessment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.06","Rice is Indonesia's staple crop, yet its productivity has declined in recent years because pest and disease outbreaks remain difficult to detect at an early stage.Existing precision agriculture approaches commonly process Internet of Things (IoT) sensor data and remote sensing imagery independently and often rely on supervised learning, requiring large amounts of labeled data.Meanwhile, multispectral drone imagery producing the Normalized Difference Vegetation Index (NDVI) provides richer information on crop physiological conditions than RGB-based vegetation indices.This study proposes Agricultural Multimodal Attention Clustering (AgriMAC), an unsupervised framework that integrates UAV derived NDVI imagery, 7-in-1 IoT soil sensor measurements, and historical weather data from the Open-Meteo API for rice field condition monitoring.Each modality is encoded using a dedicated autoencoder and fused through an entropy-regularized attention mechanism before Deep Embedded Clustering is performed.To reduce the influence of crop growth stage, the IoT representation is residualized using growth-phase statistics estimated exclusively from the training fold, enabling the discovered clusters to represent within-phase agronomic conditions rather than crop age.Experiments conducted under a grouped leave-one-field-out protocol produced a Silhouette Score of 0.465 ± 0.048, a Davies Bouldin Index of 0.807 ± 0.036, and a Calinski Harabasz Index of 195 ± 27.The learned groups also showed low normalized mutual information with growth phase (0.079 ± 0.043) and near chance phase decodability (balanced accuracy = 0.554 ± 0.042), indicating that they are only weakly associated with crop growth stage.The learned attention weights identified IoT soil measurements (0.570 ± 0.024) as the dominant modality, while NDVI imagery (0.210 ± 0.014) and weather information (0.220 ± 0.014) provided complementary spatial and temporal context.Overall, AgriMAC provides an interpretable and leakage-aware framework for multimodal clustering of rice field conditions.Although its clustering performance is comparable to that of a capacity-matched IoT-only model, it additionally quantifies the contribution of each sensing modality through attention weights and explicitly mitigates the growth-phase confound, making it suitable for field level agronomic condition monitoring and spatial decision support in precision agriculture.","水稻是印度尼西亚的主要作物，但近年来其生产力有所下降，因为病虫害暴发在早期阶段仍难以检测。现有的精准农业方法通常独立处理物联网（IoT）传感器数据和遥感影像，且往往依赖监督学习，需要大量标注数据。与此同时，生成归一化植被指数（NDVI）的多光谱无人机影像比基于RGB的植被指数能提供更丰富的作物生理状况信息。本研究提出农业多模态注意力聚类（AgriMAC），这是一个无监督框架，整合了无人机获取的NDVI影像、七合一IoT土壤传感器测量数据以及来自Open-Meteo API的历史天气数据，用于稻田状况监测。每种模态均使用专用自编码器进行编码，并通过熵正则化注意力机制进行融合，随后执行深度嵌入聚类。为减少作物生长阶段的影响，IoT表征利用仅从训练折估计的生长阶段统计量进行残差化处理，使发现的聚类能够表征阶段内的农艺状况而非作物年龄。在分组留一田块协议下进行的实验产生了0.465 ± 0.048的轮廓系数、0.807 ± 0.036的Davies-Bouldin指数和195 ± 27的Calinski-Harabasz指数。学习到的分组还显示出与生长阶段的低归一化互信息（0.079 ± 0.043）以及接近随机的阶段可解码性（平衡准确率 = 0.554 ± 0.042），表明它们与作物生长阶段仅存在弱关联。学习到的注意力权重将IoT土壤测量（0.570 ± 0.024）识别为主导模态，而NDVI影像（0.210 ± 0.014）和天气信息（0.220 ± 0.014）则提供了互补的空间和时间背景。总体而言，AgriMAC为稻田状况的多模态聚类提供了一个可解释且感知数据泄漏的框架。尽管其聚类性能与容量匹配的仅IoT模型相当，但它还通过注意力权重量化了每种传感模态的贡献，并明确减轻了生长阶段混杂因素，使其适用于田块级农艺状况监测和精准农业中的空间决策支持。",72,{"impact":110,"substance":50,"depth":18,"authority":51,"freshness":19,"relevant":21,"comment":145},"提出无监督多模态注意力聚类框架，融合无人机NDVI、IoT土壤与气象数据评估水稻健康，方法新颖且实验严谨，对精准农业田间监测有参考价值。",[147],{"name":106,"url":140},[26,27,28,149,56],"多模态融合",[151,152],"AgriMAC 水稻 多模态聚类","无人机 NDVI 水稻 病害监测","AgriMAC水稻多模态聚类-3009","10.22266\u002Fijies2026.1031.06",{"doi":154,"openalex_id":156,"authors":157,"venue":106,"cited_by_count":35,"oa_url":140,"card":164,"direction":97,"ingested_from":98},"W7213619014",[158,160,162],{"name":159,"orcid":8},"Nurfadhilah Mardianti Andini",{"name":161,"orcid":8},"Mike Yuliana",{"name":163,"orcid":8},"Moch. Zen Samsono Hadi",{"tldr":165,"method":166,"finding":167,"direction":97,"opportunity":168},"提出无监督多模态聚类框架AgriMAC，融合无人机NDVI、IoT土壤与气象数据评估水稻健康。","各模态自编码器编码，熵正则注意力融合，深度嵌入聚类，按生长阶段残差化。","聚类性能与仅IoT模型相当，但注意力权重可解释模态贡献并弱化生长阶段混淆。","可探索注意力融合机制在更多作物与传感器组合下的泛化性，并引入时序动态聚类。","2026-09-20T23:30:08.419613Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":8,"source_name":176,"source_url":173,"published_at":177,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":183,"tags":185,"search_phrases":188,"slug":191,"view_count":35,"doi":192,"paper":193,"created_at":209},2963,"A comprehensive review of deep learning methods for weed classification in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-01916-7","Weeds today are among the factors contributing to low agricultural productivity. As the world’s population continues to grow, there is an urgent need to meet global food demand. Nigeria currently lacks sufficient crop production to feed its growing population, and weeds are among the core contributors to poor agricultural yield. This study conducts a comprehensive review of Deep Learning (DL) approaches to weed classification in precision agriculture, covering literature from 2018 to 2025, was carried out. We employed a mix of quantitative and qualitative methods in the course of this review paper. Our data source is centred on Scopus-indexed papers, published with Sensors, Electronics, and Agriculture in MDPI as well as IEEE, Thomson Reuters, and Springer. The study systematically reviewed and analysed machine learning (ML), DL, and instance segmentation techniques to identify the key technological and environmental barriers, such as data limitations, class imbalance, environmental variability, and model scalability issues that affect the effectiveness and efficiency of these models when deployed in real time. These findings show that while weed management models like the YOLO variants, ResNet, and Vision Transformers achieved high accuracy in training and testing, they are associated with several challenges in their real world-deployment, such as occlusion, small object detection, and environmental adaptability. Overall, this research provides recommended solutions to enhance model robustness, scalability, and efficiency. It further provides a summary of the current state and future directions for AI-driven weed management.","杂草是当前导致农业生产力低下的因素之一。随着世界人口持续增长，满足全球粮食需求已成为迫切任务。尼日利亚目前的作物产量不足以养活其不断增长的人口，而杂草是导致农业产量低下的核心因素之一。本研究对精准农业中基于深度学习（Deep Learning，DL）的杂草分类方法进行了全面综述，涵盖2018年至2025年的文献。在综述过程中，我们采用了定量与定性相结合的方法。数据来源集中于Scopus索引论文，这些论文发表于MDPI旗下的Sensors、Electronics和Agriculture，以及IEEE、Thomson Reuters和Springer。本研究系统综述并分析了机器学习（Machine Learning，ML）、深度学习及实例分割技术，以识别影响这些模型实时部署效果与效率的关键技术和环境障碍，如数据局限性、类别不平衡、环境变异性及模型可扩展性问题。研究结果表明，尽管YOLO系列、ResNet和视觉Transformer（Vision Transformer）等杂草管理模型在训练和测试中达到了较高精度，但在实际部署中仍面临诸多挑战，如遮挡、小目标检测和环境适应性等问题。总体而言，本研究提出了增强模型鲁棒性、可扩展性和效率的推荐解决方案，并进一步总结了人工智能驱动杂草管理的现状与未来方向。","Discover Artificial Intelligence","2026-09-18T00:00:00Z",67,{"impact":110,"substance":180,"depth":49,"authority":51,"freshness":181,"relevant":21,"comment":182},18,8,"系统综述2018—2025年深度学习杂草分类方法，指出遮挡、小目标与环境适应性等落地瓶颈，对农业AI研究有参考价值，但属综述类论文、非突破性成果。",[184],{"name":176,"url":173},[26,27,186,187,29],"深度学习","杂草识别",[189,190],"深度学习 杂草分类 精准农业","YOLO 杂草识别 模型部署","深度学习杂草分类精准农业-2963","10.1007\u002Fs44163-026-01916-7",{"doi":192,"openalex_id":194,"authors":195,"venue":176,"cited_by_count":35,"oa_url":173,"card":204,"direction":134,"ingested_from":98},"W7213558223",[196,198,200,202],{"name":197,"orcid":8},"Njoku Camillus Ekene",{"name":199,"orcid":8},"Francis A. Okoye",{"name":201,"orcid":8},"Ebere Uzoka Chidi",{"name":203,"orcid":8},"OGBU MARY NNENNA",{"tldr":205,"method":206,"finding":207,"direction":134,"opportunity":208},"综述2018-2025年深度学习杂草分类方法，分析技术瓶颈并给出改进建议。","混合定量定性法，基于Scopus及MDPI、IEEE等文献，分析ML、DL与实例","YOLO、ResNet、ViT等精度高，但实际部署受遮挡、小目标与环境适应性限制。","可研究轻量化、跨域自适应模型，解决小目标与遮挡下的实时杂草识别难题。","2026-09-19T23:30:56.875661Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":8,"source_name":216,"source_url":213,"published_at":177,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":221,"tags":223,"search_phrases":225,"slug":228,"view_count":35,"doi":229,"paper":230,"created_at":260},2961,"Advances in Bioacoustics Sensing and Signal Processing Technologies","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2631-7990\u002Faea9da","Abstract Driven by advances in flexible electronics, microfabrication, and artificial intelligence, bioacoustics is revolutionizing sensing and signal processing across health, ecology, and intelligent systems. This review systematically examines key progress in high-performance acoustic sensors (e.g., piezoelectric, triboelectric, MEMS) and acquisition schemes for multi-scale biological sounds, ranging from heart tones to plant stress emissions. Furthermore, we delve into advanced methodologies for acoustic signal preprocessing, feature extraction, and classification, with a particular emphasis on the powerful analytical capabilities of multimodal data fusion and deep learning algorithms in complex real-world scenarios. Crucially, although the sensing requirements and biological targets vary drastically across medical, ecological, and agricultural domains, this review establishes a unifying framework centered on the shared physical and algorithmic challenges of bioacoustic information flow. By juxtaposing these diverse fields, the potential strategies for the interdisciplinary integration of sensing methodologies are highlighted. We also critically assess the essential roles of acoustic physical modeling, standardized testing frameworks, and gold-standard databases in evaluating system performance. Finally, the review highlights cutting-edge applications of bioacoustics in human health monitoring, medical diagnostics, human-computer interaction, and precision agriculture. By synthesizing current technological convergences and outlining future trajectories, we provide a comprehensive perspective on the promising directions and pivotal challenges facing bioacoustics research.","摘要 在柔性电子、微加工和人工智能进步的推动下，生物声学正在革新健康、生态和智能系统中的传感与信号处理。本文系统梳理了高性能声学传感器（如压电、摩擦电、MEMS）以及从心音到植物胁迫发射等多尺度生物声音采集方案的关键进展。此外，我们深入探讨了声学信号预处理、特征提取和分类的先进方法，特别强调了多模态数据融合和深度学习算法在复杂现实场景中的强大分析能力。至关重要的是，尽管医学、生态和农业领域在传感需求和生物目标上差异巨大，本文建立了一个以生物声学信息流中共同的物理和算法挑战为中心的统一框架。通过将这些不同领域并置比较，凸显了传感方法学跨学科整合的潜在策略。我们还批判性地评估了声学物理建模、标准化测试框架和金标准数据库在评估系统性能中的重要作用。最后，本文重点介绍了生物声学在人体健康监测、医学诊断、人机交互和精准农业中的前沿应用。通过综合当前技术汇聚趋势并勾勒未来轨迹，我们为生物声学研究面临的有前景方向和关键挑战提供了全面视角。","International Journal of Extreme Manufacturing",78,{"impact":180,"substance":111,"depth":180,"authority":219,"freshness":181,"relevant":21,"comment":220},14,"该综述系统梳理生物声学传感与信号处理技术，并明确指向精准农业与植物胁迫声发射监测，对农业信息化具有跨领域参考价值，但属综述类论文，非产业级突破。",[222],{"name":216,"url":213},[26,27,29,149,224],"生物声学传感",[226,227],"生物声学 传感器 信号处理","植物胁迫 声发射 监测","生物声学传感器信号处理-2961","10.1088\u002F2631-7990\u002Faea9da",{"doi":229,"openalex_id":231,"authors":232,"venue":216,"cited_by_count":35,"oa_url":213,"card":255,"direction":134,"ingested_from":98},"W7213590695",[233,236,239,241,244,247,249,252],{"name":234,"orcid":235},"Chengyu Li","https:\u002F\u002Forcid.org\u002F0000-0002-2128-3420",{"name":237,"orcid":238},"Wenbo Li","https:\u002F\u002Forcid.org\u002F0000-0003-3599-3324",{"name":240,"orcid":8},"Jingyang Wu",{"name":242,"orcid":243},"Shuo Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5411-7269",{"name":245,"orcid":246},"Han Liao","https:\u002F\u002Forcid.org\u002F0009-0001-6256-7438",{"name":248,"orcid":8},"xiang Yu",{"name":250,"orcid":251},"Cheng Li","https:\u002F\u002Forcid.org\u002F0000-0003-3424-2414",{"name":253,"orcid":254},"Xiaoming Tao","https:\u002F\u002Forcid.org\u002F0000-0002-2406-0695",{"tldr":256,"method":257,"finding":258,"direction":97,"opportunity":259},"综述生物声学传感与信号处理进展，涵盖医疗、生态与农业应用。","综述压电\u002F摩擦电\u002FMEMS声传感器、深度学习与多模态融合方法。","建立跨领域统一框架，强调物理建模、标准测试与数据库的关键作用。","植物胁迫声发射的标准化采集与深度学习分类，可填补农业声学监测空白。","2026-09-19T23:30:51.998646Z",{"id":262,"title":263,"url":264,"summary":265,"summary_zh":266,"content":8,"source_name":267,"source_url":264,"published_at":177,"category":47,"cover_url":8,"hotness":13,"is_selected":14,"score":178,"score_detail":268,"sources":270,"tags":272,"search_phrases":274,"slug":277,"view_count":35,"doi":278,"paper":279,"created_at":310},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal",{"impact":110,"substance":180,"depth":49,"authority":51,"freshness":181,"relevant":21,"comment":269},"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[271],{"name":267,"url":264},[26,27,29,56,273],"土壤碳汇",[275,276],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":278,"openalex_id":280,"authors":281,"venue":267,"cited_by_count":35,"oa_url":264,"card":305,"direction":95,"ingested_from":98},"W7213561504",[282,285,288,291,294,297,299,301,303],{"name":283,"orcid":284},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":286,"orcid":287},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":289,"orcid":290},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":292,"orcid":293},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":295,"orcid":296},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":298,"orcid":8},"Almaha Jamal",{"name":300,"orcid":8},"Afra Rashed",{"name":302,"orcid":8},"Hamda Yousif",{"name":304,"orcid":8},"Sarah Sadeq",{"tldr":306,"method":307,"finding":308,"direction":95,"opportunity":309},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z"]