[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2158":3,"related-2158":94},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":93},2158,"EfficientNet-CBAM-prototype: an attention-guided EfficientNet framework with dynamic prototype representation for tomato disease classification","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1918175","Introduction In the field of precision agriculture, one of the major hurdles is the early and accurate identification of plant diseases. Farmers may face serious irreversible loss in yield if there is a delay in diagnosis by even a few days. The CNN model has helped in improving the classification of plant diseases but faces difficulty in distinguishing fine-grained symptoms, has limited generalizability, and is not easily interpretable. Though the deep CNN model performs well in classifying plant diseases, the current methods have several flaws. Most importantly, existing algorithms misclassify visually complex samples due to their limited spatial differentiation of disease-specific morphological features, including lesion borders and necrotic regions. Classification reliability is further compromised by low inter-class embedding separability for visually comparable illness phenotypes. Apart from these representational problems, training instability is still a major problem for attention-based models. Combining randomly initialized attention modules with pretrained backbone networks causes this instability, which still limits practical application. Methods We suggest EfficientNet-CBAM-Prototype (ECP-Net), a unique end-to-end deep learning architecture, to overcome these constraints. Three complementary techniques are combined into a single framework by ECP-Net. First, parameter-efficient multi-scale feature extraction is done using an EfficientNetB0 backbone. Second, joint channel-wise and spatial feature recalibration is performed using a stabilized convolutional block attention module (CBAM). Third, a dynamic prototype memory layer uses cosine similarity-based categorization and exponential moving average (EMA) updates to maintain class-representative embedding vectors. To overcome the instability caused by randomly initialized attention weights, we further propose a two-phase training strategy wherein CBAM is frozen during phase 1 to allow prototype stabilization and then jointly fine-tuned with the learning rate in phase 2. Results Evaluated on the PlantVillage tomato subset comprising 10 disease classes across a class-balanced split of 10,000 training, 500 validation, and 500 test samples, ECP-Net achieves 98.6% test accuracy, 98.59% F1-score, and 98.65% precision with only 4.80M parameters and 85.04 ms average inference time. These results outperformed baselines including VGG16 (97.00%), ResNet50 (81.20%), MobileNetV2 (81.20%), and CNN (70.00%). Discussion Generalization is further validated on 35 real-field tomato leaf images captured under natural, uncontrolled conditions, confirming practical deployment potential.","引言 在精准农业领域，植物病害的早期准确识别是主要难题之一。若诊断延迟哪怕几天，农民可能面临严重的不可逆产量损失。CNN模型有助于改进植物病害分类，但在区分细粒度症状方面存在困难，泛化能力有限，且不易解释。尽管深度CNN模型在植物病害分类中表现良好，当前方法仍存在若干缺陷。最重要的是，现有算法由于对病害特异性形态特征（包括病斑边缘和坏死区域）的空间区分能力有限，会对视觉上复杂的样本产生误分类。对于视觉上相似的病害表型，类间嵌入可分性低，进一步损害了分类可靠性。除这些表征问题外，训练不稳定性仍是基于注意力模型的主要问题。随机初始化的注意力模块与预训练骨干网络相结合会导致这种不稳定性，这仍然限制了实际应用。方法 我们提出EfficientNet-CBAM-Prototype（ECP-Net），一种新颖的端到端深度学习架构，以克服这些限制。ECP-Net将三种互补技术结合到一个统一框架中。首先，使用EfficientNetB0骨干网络进行参数高效的多尺度特征提取。其次，使用稳定化的卷积块注意力模块（CBAM）进行通道与空间的联合特征重校准。第三，动态原型记忆层使用基于余弦相似度的分类和指数移动平均（EMA）更新来维护类代表性嵌入向量。为克服随机初始化注意力权重引起的不稳定性，我们进一步提出两阶段训练策略，其中CBAM在第一阶段冻结以使原型稳定，然后在第二阶段以学习率联合微调。结果 在PlantVillage番茄子集上评估，该子集包含10个病害类别，按类别均衡划分的10,000个训练样本、500个验证样本和500个测试样本，ECP-Net达到98.6%的测试准确率、98.59%的F1分数和98.65%的精确率，仅需4.80M参数和85.04 ms平均推理时间。这些结果优于基线，包括VGG16（97.00%）、ResNet50（81.20%）、MobileNetV2（81.20%）和CNN（70.00%）。",null,"Frontiers in Plant Science","2026-09-10T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"提出注意力引导与动态原型结合的番茄病害识别框架，在公开数据集上取得98.6%准确率并完成真实田间图像验证，方法新颖、数据扎实，对作物病害智能诊断有实用参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","深度学习","病虫害识别","番茄种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,58,63],{"code":32,"score":17,"supported":51,"title":52,"abstract":54,"tags":57},true,[53],"tomato",[55,56,53],"番茄","茄子",[30],{"code":39,"score":20,"supported":51,"title":59,"abstract":60,"tags":62},[],[28,61],"deep learning",[27,28,29],{"code":64,"score":65,"supported":51,"title":66,"abstract":67,"tags":70},"I1",7,[],[68,69],"精准农业","precision agriculture",[26],[72,73],"农业人工智能 病虫害识别 智慧农业 深度学习","农业人工智能 病虫害识别","农业人工智能病虫害识别智慧农业深度学习-2158",3,"10.3389\u002Ffpls.2026.1918175",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":84,"oa_url":6,"card":85,"direction":91,"ingested_from":92},"W7212120374",[80,82],{"name":81,"orcid":9},"E Jansi",{"name":83,"orcid":9},"Kavitha BR",0,{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"提出ECP-Net，用注意力与动态原型提升番茄病害分类精度与可解释性。","EfficientNetB0+稳定CBAM+动态原型记忆层，两阶段训练策略。","在PlantVillage番茄10类上达98.6%准确率，仅4.80M参数，优于多个基线。","农业人工智能与决策模型","可探索原型可解释性在田间复杂背景与跨域病害识别中的泛化能力。","农业遥感与作物表型","openalex","2026-09-11T23:30:25.043704Z",{"total":95,"page":21,"page_size":95,"items":96},6,[97,157,258,337,402,466],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":111,"tags":113,"primary_topic":116,"secondary_topics":117,"classification":119,"search_phrases":136,"slug":139,"view_count":84,"doi":140,"paper":141,"created_at":156},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations","2026-10-18T00:00:00Z",64,{"impact":107,"substance":17,"depth":108,"authority":13,"freshness":109,"relevant":21,"comment":110},12,16,8,"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[112],{"name":103,"url":100},[26,27,28,114,115],"番茄病害","图像识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[118],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":120},{"candidates":121,"agricultural_evidence":51},[122,127,131],{"code":32,"score":123,"supported":51,"title":124,"abstract":125,"tags":126},15,[53],[55,53],[114],{"code":39,"score":20,"supported":51,"title":128,"abstract":129,"tags":130},[],[28,61],[27,115,28],{"code":64,"score":65,"supported":51,"title":132,"abstract":133,"tags":135},[],[26,134],"smart agriculture",[26],[137,138],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":140,"openalex_id":142,"authors":143,"venue":103,"cited_by_count":84,"oa_url":149,"card":150,"direction":155,"ingested_from":92},"W7222977034",[144,146],{"name":145,"orcid":9},"Mrs. Vijetalaxmi A Hegde",{"name":147,"orcid":148},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":151,"method":152,"finding":153,"direction":89,"opportunity":154},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:10.424511Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":9,"source_name":163,"source_url":160,"published_at":164,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":165,"score_detail":166,"sources":169,"tags":171,"primary_topic":173,"secondary_topics":174,"classification":181,"search_phrases":236,"slug":239,"view_count":21,"doi":240,"paper":241,"created_at":257},4038,"Use of artificial intelligence (AI) in vegetable crops: A review","https:\u002F\u002Fdoi.org\u002F10.33545\u002F2664844x.2026.v8.i10a.1995","Artificial Intelligence (AI) is emerging technology in vegetable production, enabling data-driven decision making and precision agriculture practices. AI refers to the intelligence exhibited by machines that can perceive their environment, process large datasets, and make decisions to optimize agricultural productivity. With the integration of technologies such as machine learning, sensors, drones, satellites, Geographic Information Systems (GIS), and Global Positioning Systems (GPS), AI enables efficient monitoring and management of crop production systems In vegetable cultivation, AI technologies assist farmers in several critical operations including soil monitoring, climate assessment, pest and disease diagnosis, irrigation scheduling, and yield prediction. Sensors such as wind vane, cup count anemometer, rain gauge, lux meter, soil moisture sensor, and crop canopy sensors collect real-time environmental and crop data. These data was processed through data loggers and machine learning algorithms to provide accurate recommendations for crop management. Drone-based imaging systems and multispectral cameras further support precision farming by identifying weeds, nutrient deficiencies and disease symptoms at early stages. The use of AI in vegetables also highlights image-based predictive agriculture systems where captured images are processed through stages such as. image cleaning, feature extraction, and model training to detect pests and diseases. Several case studies demonstrate the use of AI in vegetable crops such as tomato, capsicum, and chilli for monitoring canopy growth, irrigation management. Additionally, robotic technologies in harvesting address labor shortages in agriculture. Crop yield prediction of AI in vegetables increases by 40% sensors and algorithms reduces labor cost by 30% moisture sensor increases water use efficiency by 40% spraying done reduces use of pesticide by 60% robotic arms increases harvesting accuracy by 40% artificial neural network data integration saves 25% cost in disease monitoring.","人工智能（AI）正在成为蔬菜生产领域的新兴技术，推动数据驱动决策与精准农业实践。AI是指机器所表现出的智能，能够感知环境、处理大规模数据集并做出决策以优化农业生产力。通过整合机器学习、传感器、无人机、卫星、地理信息系统（GIS）和全球定位系统（GPS）等技术，AI实现了对作物生产系统的高效监测与管理。在蔬菜种植中，AI技术可协助农民完成多项关键作业，包括土壤监测、气候评估、病虫害诊断、灌溉调度和产量预测。风向标、杯式风速计、雨量计、照度计、土壤水分传感器和作物冠层传感器等设备可采集实时的环境与作物数据。这些数据通过数据记录器和机器学习算法进行处理，为作物管理提供精准建议。基于无人机的成像系统与多光谱相机通过早期识别杂草、养分缺乏和病害症状，进一步支持精准农业。AI在蔬菜中的应用还突出了基于图像的预测性农业系统，该系统将采集的图像经过图像清洗、特征提取和模型训练等阶段处理，以检测病虫害。多个案例研究展示了AI在番茄、辣椒和尖椒等蔬菜作物中用于监测冠层生长和灌溉管理的应用。此外，收获机器人技术可缓解农业劳动力短缺问题。AI在蔬菜中的作物产量预测通过传感器和算法提高了40%，劳动力成本降低了30%，土壤水分传感器使水分利用效率提高了40%，喷雾作业使农药使用量减少了60%，机械臂使收获准确率提高了40%，人工神经网络数据集成使病害监测成本节省了25%。","International Journal of Agriculture and Food Science","2026-10-01T00:00:00Z",75,{"impact":17,"substance":167,"depth":108,"authority":107,"freshness":20,"relevant":21,"comment":168},20,"系统综述AI在蔬菜生产中的应用，含产量、成本、水肥药效等量化数据，对智慧农业主题有聚合价值。",[170],{"name":163,"url":160},[26,27,29,68,172],"蔬菜种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[175,176],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":177,"slug":178,"name":179,"path":180,"parent_code":43},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":45,"locked":14,"version":46,"method":47,"evidence":182},{"candidates":183,"agricultural_evidence":51},[184,192,198,205,209,216,223,230],{"code":39,"score":185,"supported":51,"title":186,"abstract":188,"tags":191},19,[187],"artificial intelligence",[189,190,187],"机器学习","machine learning",[27,29],{"code":32,"score":17,"supported":51,"title":193,"abstract":195,"tags":197},[194],"vegetable",[34,55,196,194,53],"辣椒",[172],{"code":177,"score":20,"supported":51,"title":199,"abstract":200,"tags":204},[],[201,202,203],"地理信息","GIS","geographic information",[],{"code":64,"score":109,"supported":51,"title":206,"abstract":207,"tags":208},[],[68,69],[26,68],{"code":210,"score":95,"supported":51,"title":211,"abstract":212,"tags":215},"F4",[],[213,214],"农药","pesticide",[],{"code":217,"score":95,"supported":51,"title":218,"abstract":219,"tags":222},"H2",[],[220,221],"土壤","soil",[],{"code":224,"score":95,"supported":51,"title":225,"abstract":226,"tags":229},"H3",[],[227,228],"灌溉","irrigation",[],{"code":231,"score":75,"supported":51,"title":232,"abstract":233,"tags":235},"I3",[],[234],"multispectral",[],[237,238],"AI 蔬菜 精准农业","蔬菜 病虫害 图像识别","AI蔬菜精准农业-4038","10.33545\u002F2664844x.2026.v8.i10a.1995",{"doi":240,"openalex_id":242,"authors":243,"venue":163,"cited_by_count":84,"oa_url":9,"card":252,"direction":89,"ingested_from":92},"W7215128283",[244,246,248,250],{"name":245,"orcid":9},"Hrishikesh Nirmala Balkrishna Thube",{"name":247,"orcid":9},"Dr. SK Kshirsagar",{"name":249,"orcid":9},"MG Agale",{"name":251,"orcid":9},"GS Shinde",{"tldr":253,"method":254,"finding":255,"direction":155,"opportunity":256},"综述人工智能在蔬菜作物生产中的应用，涵盖监测、诊断、灌溉、产量预测与采收机器人。","综述机器学习、传感器、无人机、卫星、GIS\u002FGPS及图像处理在蔬菜生产中的应用案","AI使蔬菜产量预测提高40%、节水40%、减农药60%、采收准确率提高40%。","可针对小农户蔬菜场景开发低成本多源数据融合模型，并验证AI长期经济与生态效益。","2026-10-02T23:30:40.356457Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":9,"source_name":264,"source_url":261,"published_at":265,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":271,"tags":273,"primary_topic":276,"secondary_topics":277,"classification":283,"search_phrases":310,"slug":313,"view_count":314,"doi":315,"paper":316,"created_at":336},2805,"Synergistic effects of spectral preprocessing and machine learning algorithms for nitrogen estimation in tomato using hyperspectral spectral data","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71908-1","Abstract Accurate estimation of leaf nitrogen content is essential for optimizing fertilization management and improving crop productivity. This study proposes a nondestructive hyperspectral imaging framework combined with advanced machine learning algorithms to classify nitrogen levels in tomato leaves (Solanum lycopersicum L., Royal variety). A total of 300 hyperspectral samples were collected from plants subjected to three nitrogen treatments (N-30%, N-60%, and N-90%) under controlled conditions. Reflectance data (400–1000 nm) were calibrated and preprocessed using Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky–Golay (SG) filtering. Both unsupervised and supervised algorithms were systematically evaluated. Clustering results demonstrated that preprocessing substantially influenced class separability. The MSC + GMM combination yielded the best unsupervised results, with the lowest Davies–Bouldin index (0.443) and the highest silhouette coefficient (0.975), indicating improved cluster compactness and separation. In supervised learning, Neural Networks (NN) consistently outperformed the other models, achieving 100% accuracy and an ROC–AUC of 1.00 under SNV, MSC, and SG preprocessing. Gradient Boosting (GB) and Support Vector Machine (SVM) also demonstrated strong predictive capability, whereas conventional models, including LDA, LR, KNN, and NB, showed moderate improvements following preprocessing. Statistical analysis confirmed the significant effect of spectral preprocessing on both clustering and classification outcomes ( p \u003C 0.05). Overall, integrating hyperspectral imaging with appropriate preprocessing and nonlinear machine learning models provided strong classification performance under the controlled experimental conditions. However, the exceptionally high performance observed for some models should be interpreted cautiously because of the relatively small dataset and experimentally controlled nitrogen treatments. Moreover, the absence of an independent external validation dataset limits the assessment of generalizability across growing conditions, cultivars, and field environments. Therefore, independent validation using larger and more diverse datasets is required before broader application of the proposed framework in practical precision agriculture.","摘要 准确估算叶片氮含量对于优化施肥管理和提高作物生产力至关重要。本研究提出了一种无损高光谱成像框架，结合先进的机器学习算法对番茄叶片（Solanum lycopersicum L.，Royal品种）的氮水平进行分类。在受控条件下，从接受三种氮处理（N-30%、N-60%和N-90%）的植株中共采集了300个高光谱样本。反射率数据（400–1000 nm）经标准正态变量变换（SNV）、多元散射校正（MSC）和Savitzky–Golay（SG）滤波进行校准和预处理。系统评估了无监督和有监督算法。聚类结果表明，预处理对类别可分性产生了显著影响。MSC + GMM组合取得了最佳无监督结果，具有最低的Davies–Bouldin指数（0.443）和最高的轮廓系数（0.975），表明聚类紧密度和分离度得到改善。在有监督学习中，神经网络（NN）始终优于其他模型，在SNV、MSC和SG预处理下均达到100%的准确率和1.00的ROC–AUC。梯度提升（GB）和支持向量机（SVM）也表现出较强的预测能力，而传统模型（包括LDA、LR、KNN和NB）在预处理后表现出中等程度的改善。统计分析证实了光谱预处理对聚类和分类结果均有显著影响（p \u003C 0.05）。总体而言，将高光谱成像与适当的预处理及非线性机器学习模型相结合，在受控实验条件下提供了较强的分类性能。然而，由于数据集相对较小且氮处理为实验受控条件，某些模型所表现出的极高性能应谨慎解读。此外，缺乏独立的外部验证数据集限制了对跨生长条件、品种和田间环境泛化能力的评估。因此，在将该框架广泛应用于实际精准农业之前，需要使用更大规模、更多样化的数据集进行独立验证。","Scientific Reports","2026-09-17T00:00:00Z",77,{"impact":123,"substance":268,"depth":269,"authority":19,"freshness":13,"relevant":21,"comment":270},21,17,"高光谱结合机器学习实现番茄叶片氮素无损估测，方法系统、结论明确，但样本量小且缺乏外部验证，属细分领域方法学进展。",[272],{"name":264,"url":261},[26,27,274,30,275],"精准施肥","高光谱遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[278,279],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":231,"slug":280,"name":281,"path":282,"parent_code":43},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":45,"locked":14,"version":46,"method":47,"evidence":284},{"candidates":285,"agricultural_evidence":51},[286,290,294,299,306],{"code":32,"score":123,"supported":51,"title":287,"abstract":288,"tags":289},[53],[55,53],[30],{"code":39,"score":123,"supported":51,"title":291,"abstract":292,"tags":293},[190],[189,190],[27],{"code":231,"score":107,"supported":51,"title":295,"abstract":297,"tags":298},[296],"hyperspectral",[296],[275],{"code":300,"score":65,"supported":51,"title":301,"abstract":302,"tags":305},"F3",[],[303,304],"施肥","fertilization",[274],{"code":64,"score":65,"supported":51,"title":307,"abstract":308,"tags":309},[],[68,69],[26],[311,312],"农业人工智能 高光谱遥感 智慧农业 番茄种植","农业人工智能 高光谱遥感","农业人工智能高光谱遥感智慧农业番茄种植-2805",2,"10.1038\u002Fs41598-026-71908-1",{"doi":315,"openalex_id":317,"authors":318,"venue":264,"cited_by_count":84,"oa_url":261,"card":331,"direction":89,"ingested_from":92},"W7213472002",[319,322,325,328],{"name":320,"orcid":321},"Mohammad Vahedi Torshizi","https:\u002F\u002Forcid.org\u002F0000-0003-3648-1515",{"name":323,"orcid":324},"Sajad Sabzi","https:\u002F\u002Forcid.org\u002F0000-0003-2439-5329",{"name":326,"orcid":327},"Mohsen Azadbakht","https:\u002F\u002Forcid.org\u002F0000-0002-5726-9321",{"name":329,"orcid":330},"Razieh Pourdarbani","https:\u002F\u002Forcid.org\u002F0000-0003-0766-8305",{"tldr":332,"method":333,"finding":334,"direction":91,"opportunity":335},"用高光谱成像结合预处理与机器学习，对番茄叶片氮素水平进行分类。","300个高光谱样本，SNV、MSC、SG预处理，聚类与多种监督分类算法对比。","MSC+GMM聚类最优，神经网络在三种预处理下均达100%准确率。","样本少且无外部验证，可扩展多品种、多环境田间数据并做独立验证以提升泛化性。","2026-09-17T23:30:59.361085Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":9,"content":9,"source_name":342,"source_url":9,"published_at":343,"category":12,"cover_url":9,"hotness":344,"is_selected":14,"score":345,"score_detail":346,"sources":348,"tags":353,"primary_topic":356,"secondary_topics":357,"classification":359,"search_phrases":375,"slug":378,"view_count":75,"doi":379,"paper":380,"created_at":401},1817,"Advances in Recognition Methods for Fruit and Vegetable Harvesting（MDPI Agriculture 2026, 16(17), 1924）","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1924","江苏大学农业工程学院综述果蔬目标识别领域近期文献，总结数字图像处理、传统机器学习和深度学习等主流方法的实施原理与改进方向，指出当前技术在算法模型实时性、稳健性、泛化能力方面仍面临挑战。未来目标识别技术有望通过多模态特征融合、大规模模型和半监督学习实现突破，向更高精度、更快处理速度和更易部署方向演进，为智慧农业大规模实施提供技术支持。","MDPI Agriculture 2026, 16(17)","2026-09-05T00:00:00Z",25,72,{"impact":123,"substance":167,"depth":17,"authority":107,"freshness":65,"relevant":21,"comment":347},"综述性论文，系统梳理果蔬采摘识别技术进展与挑战，对智慧农业细分领域有参考价值。",[349,350],{"name":342,"url":340},{"name":351,"url":352},"Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16171924",[26,27,28,354,355],"果蔬采摘","目标识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[358],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":360},{"candidates":361,"agricultural_evidence":51},[362,366,370],{"code":32,"score":109,"supported":51,"title":363,"abstract":364,"tags":365},[194],[],[],{"code":39,"score":109,"supported":51,"title":367,"abstract":368,"tags":369},[],[28,189],[27,28],{"code":64,"score":371,"supported":51,"title":372,"abstract":373,"tags":374},4,[],[26],[26],[376,377],"农业人工智能 智慧农业 果蔬采摘 深度学习","农业人工智能 智慧农业","农业人工智能智慧农业果蔬采摘深度学习-1817","10.3390\u002Fagriculture16171924",{"doi":379,"openalex_id":381,"authors":382,"venue":351,"cited_by_count":84,"oa_url":352,"card":396,"direction":155,"ingested_from":92},"W7211911520",[383,386,388,391,394],{"name":384,"orcid":385},"Dianlei Han","https:\u002F\u002Forcid.org\u002F0000-0002-3798-5515",{"name":387,"orcid":9},"Shixing Xu",{"name":389,"orcid":390},"Siyu Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-4638-4137",{"name":392,"orcid":393},"Qingzhen Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-5122-9759",{"name":395,"orcid":9},"Xuegeng Chen",{"tldr":397,"method":398,"finding":399,"direction":155,"opportunity":400},"综述果蔬采摘目标识别方法，分析数字图像、机器学习与深度学习技术，指出挑战与未来方向。","综述数字图像处理、传统机器学习、深度学习等方法，分析其原理与改进。","当前技术实时性、稳健性、泛化能力不足，未来需多模态融合、大模型和半监督学习。","可研究轻量化深度学习模型与多模态融合，提升果蔬采摘识别在复杂环境下的实时性与泛化能力。","2026-09-07T00:04:41.651611Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":408,"source_url":405,"published_at":409,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":410,"score_detail":411,"sources":413,"tags":415,"primary_topic":417,"secondary_topics":418,"classification":420,"search_phrases":437,"slug":440,"view_count":21,"doi":441,"paper":442,"created_at":465},4494,"Tomato ripeness classification with weight-based and texture-adaptive pooling in CNNs","https:\u002F\u002Fdoi.org\u002F10.64960\u002Feasr.2026.266223","This research introduces a weight-based mixed pooling and adaptive fuzzy pooling mechanism based on feature map texture analysis for attention mechanism Squeeze-and-Excitation Networks (SE) and Convolutional Neural Networks (CNNs) in tomato ripeness classification. Traditional CNNs and SE networks rely on deterministic pooling strategies that can be suboptimal for capturing diverse visual features such as texture and color gradients in fruit. A novel fuzzy pooling system is proposed that dynamically adjusts the fusion ratio of min and max pooling based on real-time texture analysis of feature maps via a Laplacian filter. This mechanism was integrated into Darknet53, ResNet34, and ResNet101 architectures, both directly and within the SE attention block. The method was evaluated on a five-class tomato ripeness dataset using 10-fold cross-validation against traditional and fixed-weight pooling methods. The fuzzy and mixed pooling approach demonstrated superior performance on certain models which include ESEnet: Darknet53 + SE (Fuzzy pool) achieved an average F1 score across folds of 0.8429, outperforming Darknet53 + baseline SE (Average pool) at 0.8350; Extended Net: ResNet34 + Fuzzy pool achieved an average F1 score across folds of 0.8305, outperforming baseline ResNet34 at 0.8130 and ResNet101 + Fuzzy pool achieved an average F1 score across folds of 0.8284, outperforming baseline ResNet101 at 0.8267. This improvement was realized with minimal increase in computational overhead. Specifically, measurements of model parameters (M) and FLOPs (GMACs) showed only a slight increase across all architectures after integrating SEnet into the system, and zero increase after integrating the Laplacian filter and fuzzy logic. The study concludes that logic-based adaptive pooling is a robust strategy that improves feature representation in CNNs, achieving a balance between classification accuracy and computational cost. These findings offer practical value for developing more reliable automated harvesting systems and provide a generalizable enhancement for a wide range of computer vision classification tasks.","本研究提出了一种基于特征图纹理分析的权重混合池化与自适应模糊池化机制，用于番茄成熟度分类中的注意力机制挤压激励网络（Squeeze-and-Excitation Networks, SE）和卷积神经网络（Convolutional Neural Networks, CNNs）。传统的CNN和SE网络依赖确定性池化策略，在捕捉果实纹理和颜色梯度等多样化视觉特征时可能并非最优。本文提出了一种新颖的模糊池化系统，通过拉普拉斯滤波器对特征图进行实时纹理分析，动态调整最小池化和最大池化的融合比例。该机制被集成到Darknet53、ResNet34和ResNet101架构中，既可直接集成，也可嵌入SE注意力模块内。该方法在五类番茄成熟度数据集上采用10折交叉验证，与传统池化及固定权重池化方法进行了对比评估。模糊池化和混合池化方法在部分模型上表现出优越性能，包括：ESEnet：Darknet53 + SE（模糊池化）在各折上的平均F1得分为0.8429，优于Darknet53 + 基线SE（平均池化）的0.8350；扩展网络：ResNet34 + 模糊池化在各折上的平均F1得分为0.8305，优于基线ResNet34的0.8130；ResNet101 + 模糊池化在各折上的平均F1得分为0.8284，优于基线ResNet101的0.8267。这一改进仅带来了极小的计算开销增加。具体而言，模型参数量（M）和浮点运算次数（GMACs）的测量结果表明，将SEnet集成到系统后所有架构仅有轻微增加，而集成拉普拉斯滤波器和模糊逻辑后则无任何增加。研究结论认为，基于逻辑的自适应池化是一种鲁棒策略，能够改善CNN中的特征表示，在分类精度和计算成本之间实现平衡。这些发现为开发更可靠的自动化收获系统提供了实用价值，并为广泛的计算机视觉分类任务提供了一种可推广的增强方法。","Engineering and Applied Science Research","2026-10-07T00:00:00Z",66,{"impact":109,"substance":167,"depth":269,"authority":107,"freshness":20,"relevant":21,"comment":412},"提出基于纹理自适应模糊池化的CNN改进方法，在番茄成熟度分类上取得小幅但稳定的F1提升且算力开销几乎不增，对自动采摘与农业视觉识别有参考价值，但属细分方法类论文，影响面有限。",[414],{"name":408,"url":405},[26,27,55,115,416],"自动采摘",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[419],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":421},{"candidates":422,"agricultural_evidence":51},[423,427,433],{"code":32,"score":123,"supported":51,"title":424,"abstract":425,"tags":426},[53],[55,53],[55],{"code":39,"score":109,"supported":51,"title":428,"abstract":429,"tags":432},[],[430,431],"计算机视觉","computer vision",[27,115],{"code":64,"score":21,"supported":14,"title":434,"abstract":435,"tags":436},[],[],[26],[438,439],"番茄 成熟度 分类 卷积神经网络","Darknet53 ResNet 番茄 成熟度","番茄成熟度分类卷积神经网络-4494","10.64960\u002Feasr.2026.266223",{"doi":441,"openalex_id":443,"authors":444,"venue":408,"cited_by_count":84,"oa_url":459,"card":460,"direction":155,"ingested_from":92},"W7220840158",[445,447,449,451,454,456],{"name":446,"orcid":9},"Rut Yatigul",{"name":448,"orcid":9},"Wisanu Jitviriya",{"name":450,"orcid":9},"Eiji Hayashi",{"name":452,"orcid":453},"Aran Blattler","https:\u002F\u002Forcid.org\u002F0000-0002-8684-6239",{"name":455,"orcid":9},"Watcharin Tangsuksant",{"name":457,"orcid":458},"Teppakorn Sittiwanchai","https:\u002F\u002Forcid.org\u002F0000-0001-9653-8750","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F266223\u002F176569",{"tldr":461,"method":462,"finding":463,"direction":89,"opportunity":464},"提出基于纹理自适应模糊池化机制，提升CNN番茄成熟度分类性能。","用Laplacian滤波分析特征图纹理，动态调整最大最小池化融合比，集成到Dar","模糊池化在多个模型上F1提升，且计算开销几乎不增加。","可探索自适应池化在其它果蔬成熟度或田间复杂光照下的泛化能力。","2026-10-09T23:30:09.993787Z",{"id":467,"title":468,"url":469,"summary":470,"summary_zh":471,"content":9,"source_name":472,"source_url":469,"published_at":473,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":474,"score_detail":475,"sources":477,"tags":479,"primary_topic":483,"secondary_topics":484,"classification":486,"search_phrases":507,"slug":510,"view_count":75,"doi":511,"paper":512,"created_at":526},3284,"Computer vision-based size estimation module for selective harvesting of lettuce","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.14546","Selective harvesting of leafy vegetables remains a major challenge in agricultural automation, particularly for lettuce that meet predefined size or maturity criteria. Vision-based perception systems play a critical role in enabling selective harvesting by identifying plants that meet predefined size or maturity criteria. This study presents the development and validation of computer vision-based size estimation software designed to support selective harvesting decisions in leafy vegetable production. A C #-based image processing application was developed to perform red, green and blue (RGB) colour segmentation, contour detection and pixel-to-metric size estimation using images acquired from a standard Universal Serial Bus (USB) webcam. The system converts pixel measurements into real-world dimensions through calibrated scaling factors that account for variations in camera-to-plant distance. Field experiments were conducted using three commercial lettuce cultivars Romaine, Iceberg and Curly. Vision-based canopy width measurements showed strong agreement with manual measurements (N = 60) and exhibited very strong correlations with plant weight (Romaine type: r = 0.9803; Iceberg type: r = 0.9935; Curly type: r = 0.9707; p \u003C 0.001). Linear regression models achieved high predictive performance (R² = 0.942–0.987), while error metrics indicated low prediction errors (Mean absolute error (MAE): 5.62–8.64 g; root mean square error (RMSE): 7.30–11.06 g). Bland-Altman analysis further confirmed strong agreement between vision-based estimates and manual measurements. This study primarily validates a lightweight vision-based measurement algorithm intended for future integration into robotic harvesting systems. The results demonstrate that simple RGB-based image processing can provide reliable size estimation for selective harvesting decisions in leafy vegetable production systems.","叶菜类蔬菜的选择性收获仍是农业自动化领域的一项重大挑战，尤其是对符合预定大小或成熟度标准的生菜而言。基于视觉的感知系统通过识别符合预定大小或成熟度标准的植株，在实现选择性收获中发挥着关键作用。本研究介绍了一款基于计算机视觉的大小估算软件的开发与验证，该软件旨在为叶菜类蔬菜生产中的选择性收获决策提供支持。研究开发了一款基于C#的图像处理应用程序，利用标准通用串行总线（USB）网络摄像头采集的图像，执行红绿蓝（RGB）颜色分割、轮廓检测以及像素到实际尺寸的估算。该系统通过经过校准的缩放因子将像素测量值转换为实际尺寸，并考虑了摄像头与植株之间距离的变化。田间试验采用三个商业化生菜品种——罗马生菜、结球生菜和皱叶生菜。基于视觉的冠幅测量结果与人工测量结果高度一致（N = 60），并与植株重量呈现极强的相关性（罗马型：r = 0.9803；结球型：r = 0.9935；皱叶型：r = 0.9707；p \u003C 0.001）。线性回归模型取得了较高的预测性能（R² = 0.942–0.987），误差指标显示预测误差较低（平均绝对误差（MAE）：5.62–8.64 g；均方根误差（RMSE）：7.30–11.06 g）。Bland-Altman分析进一步证实了基于视觉的估算值与人工测量值之间的高度一致性。本研究主要验证了一种轻量级视觉测量算法，旨在未来集成到机器人收获系统中。结果表明，简单的基于RGB的图像处理能够为叶菜类蔬菜生产系统中的选择性收获决策提供可靠的大小估算。","Plant Science Today","2026-09-22T00:00:00Z",69,{"impact":107,"substance":167,"depth":108,"authority":107,"freshness":20,"relevant":21,"comment":476},"该研究开发了基于RGB图像的轻量级生菜尺寸估计模块，与人工测量高度一致，为机器人选择性采收提供了可靠感知方案，具有较高的技术参考价值。",[478],{"name":472,"url":469},[26,27,480,481,482],"生菜","机器视觉","选择性采收",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[485],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":487},{"candidates":488,"agricultural_evidence":51},[489,493,497,503],{"code":39,"score":108,"supported":51,"title":490,"abstract":491,"tags":492},[431],[430,431],[27,481],{"code":32,"score":13,"supported":51,"title":494,"abstract":495,"tags":496},[],[34,480,194],[480],{"code":498,"score":75,"supported":51,"title":499,"abstract":500,"tags":502},"F6",[],[501],"robotic harvesting",[],{"code":64,"score":21,"supported":14,"title":504,"abstract":505,"tags":506},[],[],[26],[508,509],"生菜 选择性采收 机器视觉","计算机视觉 生菜 尺寸估计","生菜选择性采收机器视觉-3284","10.14719\u002Fpst.14546",{"doi":511,"openalex_id":513,"authors":514,"venue":472,"cited_by_count":84,"oa_url":469,"card":521,"direction":89,"ingested_from":92},"W7214000589",[515,518],{"name":516,"orcid":517},"Erhan Kahya","https:\u002F\u002Forcid.org\u002F0000-0001-7768-9190",{"name":519,"orcid":520},"Fatma Funda Özdüven","https:\u002F\u002Forcid.org\u002F0000-0003-4286-8943",{"tldr":522,"method":523,"finding":524,"direction":89,"opportunity":525},"开发基于RGB图像处理的生菜尺寸估计软件，用于选择性采收决策。","C#图像处理，USB摄像头采集，RGB分割与轮廓检测，标定像素-实际尺寸转换。","视觉冠幅与重量强相关（r=0.97-0.99），回归模型R²达0.94-0.99，误差低。","可集成到机器人采收系统，并探索多品种、多光照条件下的鲁棒性及实时边缘部署。","2026-09-23T23:30:38.203393Z"]