[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4074":3,"related-4074":97},{"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":38,"search_phrases":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":96},4074,"Automated Diagnosis of Tomato Leaf Diseases with Attention-Enhanced Feature Aggregation","https:\u002F\u002Fdoi.org\u002F10.35377\u002Fsaucis...1826329","Tomato crop yield can be enhanced using agricultural technologies if the diseases associated with the plant leaves are detected early. This article proposes a new tomato disease classification model, known as the Multiscale Parallel Feature Aggregation Network with Attention Fusion (MPFAN-AF), which can classify diseases using leaf images. This model comprises parallel convolutional branches that extract multiscale features to retrieve rich data and fuse them through an attention-based fusion module, which performs global average pooling and weights the channels. This model enables the network to optimise patterns associated with the disease while disregarding the diseased area's background noise. The generalisation ability is increased using dropout and L2 weight decay. The refined features are passed through a lightweight multi-layer perceptron for classification. Evaluated on a benchmark tomato leaf disease dataset, MPFAN-AF outperforms conventional Convolutional Neural Networks (CNNs) and existing attention-based models across accuracy, precision, recall, and F1-score. Overall, MPFAN-AF delivers an efficient, accurate, and interpretable solution for automated disease diagnosis in precision agriculture.","如果及早检测出与植物叶片相关的病害，就可以利用农业技术提高番茄作物产量。本文提出了一种新的番茄病害分类模型，称为基于注意力融合的多尺度并行特征聚合网络（MPFAN-AF），可利用叶片图像对病害进行分类。该模型包含并行卷积分支，用于提取多尺度特征以获取丰富数据，并通过基于注意力的融合模块进行融合，该模块执行全局平均池化并对通道进行加权。该模型使网络能够优化与病害相关的模式，同时忽略病区背景噪声。通过dropout和L2权重衰减提高了泛化能力。精炼后的特征通过轻量级多层感知机进行分类。在基准番茄叶片病害数据集上评估时，MPFAN-AF在准确率、精确率、召回率和F1分数方面均优于传统卷积神经网络（CNN）和现有基于注意力的模型。总体而言，MPFAN-AF为精准农业中的自动化病害诊断提供了一种高效、准确且可解释的解决方案。",null,"Sakarya University Journal of Computer and Information Sciences","2026-09-30T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,20,17,9,1,"提出注意力融合多尺度特征网络用于番茄叶病自动诊断，方法有创新且指标优于现有模型，但属单篇学术论文，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准农业","番茄病害","图像识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,59],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":51},15,true,[48],"tomato",[50,48],"番茄",[29],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":58},"I1",8,[],[28,57],"precision agriculture",[26,28],{"code":60,"score":61,"supported":14,"title":62,"abstract":63,"tags":64},"I2",2,[],[],[27,30],[66,67],"番茄叶片病害 识别 模型","MPFAN-AF 番茄病害","番茄叶片病害识别模型-4074",4,"10.35377\u002Fsaucis...1826329",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":86,"oa_url":87,"card":88,"direction":94,"ingested_from":95},"W7214991700",[74,77,80,83],{"name":75,"orcid":76},"Mohd Aquib Ansari","https:\u002F\u002Forcid.org\u002F0000-0002-9083-1523",{"name":78,"orcid":79},"Shahnawaz Ahmad","https:\u002F\u002Forcid.org\u002F0000-0002-3989-9928",{"name":81,"orcid":82},"Arvind Mewada","https:\u002F\u002Forcid.org\u002F0000-0002-4680-611X",{"name":84,"orcid":85},"Harsh Pratap Singh","https:\u002F\u002Forcid.org\u002F0000-0001-5044-8258",0,"https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5432390",{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"提出MPFAN-AF模型，用多尺度并行卷积和注意力融合实现番茄叶片病害自动分类。","多尺度并行卷积分支提取特征，注意力融合模块加权通道，dropout和L2正则化。","在基准数据集上，MPFAN-AF在准确率、精确率、召回率和F1上均优于传统CNN和现有注意力模型。","农业人工智能与决策模型","可探索轻量化模型在移动端或边缘设备的部署，以及多作物病害的跨域泛化能力。","智慧农业 \u002F 农业物联网","openalex","2026-10-03T23:30:18.708398Z",{"total":98,"page":21,"page_size":98,"items":99},6,[100,162,226,328,376,435],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":113,"tags":115,"primary_topic":117,"secondary_topics":118,"classification":124,"search_phrases":142,"slug":145,"view_count":86,"doi":146,"paper":147,"created_at":161},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":17,"substance":110,"depth":111,"authority":13,"freshness":54,"relevant":21,"comment":112},18,16,"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。",[114],{"name":106,"url":103},[26,27,116,29,30],"深度学习",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[119],{"code":60,"slug":120,"name":121,"path":122,"parent_code":123},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":125},{"candidates":126,"agricultural_evidence":46},[127,131,136],{"code":32,"score":45,"supported":46,"title":128,"abstract":129,"tags":130},[48],[50,48],[29],{"code":60,"score":20,"supported":46,"title":132,"abstract":133,"tags":135},[],[116,134],"deep learning",[27,30,116],{"code":53,"score":137,"supported":46,"title":138,"abstract":139,"tags":141},7,[],[26,140],"smart agriculture",[26],[143,144],"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":146,"openalex_id":148,"authors":149,"venue":106,"cited_by_count":86,"oa_url":155,"card":156,"direction":94,"ingested_from":95},"W7222977034",[150,152],{"name":151,"orcid":9},"Mrs. Vijetalaxmi A Hegde",{"name":153,"orcid":154},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":157,"method":158,"finding":159,"direction":92,"opportunity":160},"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。","2026-10-20T23:30:10.424511Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":9,"source_name":168,"source_url":165,"published_at":169,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":170,"score_detail":171,"sources":173,"tags":175,"primary_topic":177,"secondary_topics":178,"classification":180,"search_phrases":197,"slug":200,"view_count":21,"doi":201,"paper":202,"created_at":225},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":54,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":172},"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。",[174],{"name":168,"url":165},[26,27,50,30,176],"自动采摘",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[179],{"code":60,"slug":120,"name":121,"path":122,"parent_code":123},{"status":39,"locked":14,"version":40,"method":41,"evidence":181},{"candidates":182,"agricultural_evidence":46},[183,187,193],{"code":32,"score":45,"supported":46,"title":184,"abstract":185,"tags":186},[48],[50,48],[50],{"code":60,"score":54,"supported":46,"title":188,"abstract":189,"tags":192},[],[190,191],"计算机视觉","computer vision",[27,30],{"code":53,"score":21,"supported":14,"title":194,"abstract":195,"tags":196},[],[],[26],[198,199],"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":201,"openalex_id":203,"authors":204,"venue":168,"cited_by_count":86,"oa_url":219,"card":220,"direction":94,"ingested_from":95},"W7220840158",[205,207,209,211,214,216],{"name":206,"orcid":9},"Rut Yatigul",{"name":208,"orcid":9},"Wisanu Jitviriya",{"name":210,"orcid":9},"Eiji Hayashi",{"name":212,"orcid":213},"Aran Blattler","https:\u002F\u002Forcid.org\u002F0000-0002-8684-6239",{"name":215,"orcid":9},"Watcharin Tangsuksant",{"name":217,"orcid":218},"Teppakorn Sittiwanchai","https:\u002F\u002Forcid.org\u002F0000-0001-9653-8750","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F266223\u002F176569",{"tldr":221,"method":222,"finding":223,"direction":92,"opportunity":224},"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":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":9,"source_name":232,"source_url":229,"published_at":233,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":234,"score_detail":235,"sources":237,"tags":239,"primary_topic":242,"secondary_topics":243,"classification":250,"search_phrases":306,"slug":309,"view_count":21,"doi":310,"paper":311,"created_at":327},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":110,"substance":18,"depth":111,"authority":17,"freshness":20,"relevant":21,"comment":236},"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。",[238],{"name":232,"url":229},[26,27,240,28,241],"病虫害识别","蔬菜种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[244,245],{"code":60,"slug":120,"name":121,"path":122,"parent_code":123},{"code":246,"slug":247,"name":248,"path":249,"parent_code":123},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":39,"locked":14,"version":40,"method":41,"evidence":251},{"candidates":252,"agricultural_evidence":46},[253,261,267,274,278,285,292,299],{"code":60,"score":254,"supported":46,"title":255,"abstract":257,"tags":260},19,[256],"artificial intelligence",[258,259,256],"机器学习","machine learning",[27,240],{"code":32,"score":110,"supported":46,"title":262,"abstract":264,"tags":266},[263],"vegetable",[34,50,265,263,48],"辣椒",[241],{"code":246,"score":20,"supported":46,"title":268,"abstract":269,"tags":273},[],[270,271,272],"地理信息","GIS","geographic information",[],{"code":53,"score":54,"supported":46,"title":275,"abstract":276,"tags":277},[],[28,57],[26,28],{"code":279,"score":98,"supported":46,"title":280,"abstract":281,"tags":284},"F4",[],[282,283],"农药","pesticide",[],{"code":286,"score":98,"supported":46,"title":287,"abstract":288,"tags":291},"H2",[],[289,290],"土壤","soil",[],{"code":293,"score":98,"supported":46,"title":294,"abstract":295,"tags":298},"H3",[],[296,297],"灌溉","irrigation",[],{"code":300,"score":301,"supported":46,"title":302,"abstract":303,"tags":305},"I3",3,[],[304],"multispectral",[],[307,308],"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":310,"openalex_id":312,"authors":313,"venue":232,"cited_by_count":86,"oa_url":9,"card":322,"direction":92,"ingested_from":95},"W7215128283",[314,316,318,320],{"name":315,"orcid":9},"Hrishikesh Nirmala Balkrishna Thube",{"name":317,"orcid":9},"Dr. SK Kshirsagar",{"name":319,"orcid":9},"MG Agale",{"name":321,"orcid":9},"GS Shinde",{"tldr":323,"method":324,"finding":325,"direction":94,"opportunity":326},"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":329,"title":330,"url":331,"summary":332,"summary_zh":9,"content":9,"source_name":333,"source_url":9,"published_at":334,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":335,"score_detail":336,"sources":340,"tags":342,"primary_topic":345,"secondary_topics":346,"classification":347,"search_phrases":363,"slug":366,"view_count":61,"doi":9,"paper":367,"created_at":375},3047,"基于CNN\u002FLR\u002FGA-BP的辣椒叶病识别稳定性与精度多维度视觉特征比较","https:\u002F\u002Fwww.mdpi.com\u002F2311-7524\u002F12\u002F9\u002F1176","塔里木大学Xueting Ma、Yifei Li等联合东北林业大学、南京农业大学，构建1260份辣椒叶数据集（健康、细菌性斑点病、黄化卷叶病），采用Lab b-channel、RGB super-green、Otsu-ACWE三种分割算法系统评估最优预处理方案，提取32个融合视觉特征并以随机森林消除7个低贡献冗余特征保留25个判别变量。20次独立重复试验表明：CNN测试平均精度97.67%、平均AUC 0.999，稳定性最佳；LR计算成本低适合资源受限场景；GA-BP非线性拟合能力弱、预测波动严重。该研究为辣椒叶病识别提供标准化实验框架。","MDPI Horticulturae 12(9):1176","2026-09-19T00:00:00Z",78,{"impact":111,"substance":337,"depth":110,"authority":338,"freshness":54,"relevant":21,"comment":339},22,14,"基于1260份辣椒叶数据集系统比较CNN\u002FLR\u002FGA-BP三种模型，方法规范、结论可靠，对作物病害智能识别有参考价值。",[341],{"name":333,"url":331},[26,27,30,343,344],"辣椒叶病","病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":348},{"candidates":349,"agricultural_evidence":46},[350,355,359],{"code":32,"score":351,"supported":46,"title":352,"abstract":353,"tags":354},11,[265],[265],[],{"code":60,"score":61,"supported":14,"title":356,"abstract":357,"tags":358},[],[],[27,30],{"code":53,"score":21,"supported":14,"title":360,"abstract":361,"tags":362},[],[],[26],[364,365],"塔里木大学 辣椒叶病 识别","CNN 辣椒叶病 视觉特征","塔里木大学辣椒叶病识别-3047",{"doi":9,"openalex_id":9,"authors":368,"venue":9,"cited_by_count":86,"oa_url":9,"card":369,"direction":92,"ingested_from":374},[],{"tldr":370,"method":371,"finding":372,"direction":92,"opportunity":373},"比较CNN、LR、GA-BP对辣椒叶病的识别精度与稳定性，并评估三种分割预处理方案。","1260份辣椒叶图像，Lab\u002FRGB\u002FOtsu-ACWE分割，32特征经随机森林","CNN平均精度97.67%、AUC 0.999且最稳定；LR成本低适合资源受限；GA-BP波动严重。","可探索轻量化CNN与LR融合的田间实时识别方案，并验证多作物、多病害下的泛化稳定性。","agent","2026-09-21T00:04:39.222838Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":9,"source_name":382,"source_url":379,"published_at":383,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":384,"score_detail":385,"sources":388,"tags":390,"primary_topic":393,"secondary_topics":394,"classification":395,"search_phrases":411,"slug":414,"view_count":407,"doi":415,"paper":416,"created_at":434},2962,"A Multi-Modal Generative Model for Tomato Disease Leaves Understanding","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.19555","Artificial intelligence for plant disease analysis has advanced from task-specific classifiers to multi-modal models capable of jointly interpreting visual and textual information. However, practical deployment in precision agriculture remains limited because most existing approaches treat disease understanding as isolated prediction tasks, failing to capture the complementary relationships among symptom recognition, severity assessment, and question-driven diagnostic reasoning. In tomato pathology, accurate interpretation of diseased leaves requires more than label prediction; it demands integrating visual symptoms with semantic context to support a comprehensive and explainable understanding. Here, we present SOLAR, a multimodal generative model that understands tomato disease spanning six question-answering tasks. SOLAR learns to align visual features with task-aware language representations by Fusion Expert module based on mixture-of-expert, enabling it to generate contextually relevant answers across diverse diagnostic tasks. By formulating tomato disease analysis as a generative Visual Question Answering (VQA) task, SOLAR provides a flexible framework that supports multi-task inference within a single model while improving performance and cross-task knowledge sharing. We evaluate SOLAR on $41,677$ images, including $216,209$ Question-Answering (QA) pairs to understand tomato leaf disease under both closed and open-ended QA settings. Experimental results show that SOLAR consistently outperforms state-of-the-art vision-only, vision-language, and task-specific models across all tasks, demonstrating superior accuracy, robustness, and multimodal reasoning. These findings highlight the potential of generative multimodal modeling as an effective direction for understanding of plant disease. The code for this study is available at https:\u002F\u002Fgithub.com\u002FEnalisUs\u002FSOLAR.","人工智能用于植物病害分析已从任务专用分类器发展到能够联合解读视觉与文本信息的多模态模型。然而，在精准农业中的实际部署仍然有限，因为大多数现有方法将病害理解视为孤立的预测任务，未能捕捉症状识别、严重程度评估与问题驱动诊断推理之间的互补关系。在番茄病理学中，对病叶的准确解读需要的不仅仅是标签预测；它要求将视觉症状与语义上下文相结合，以支持全面且可解释的理解。在此，我们提出SOLAR，一种多模态生成模型，能够理解番茄病害并涵盖六项问答任务。SOLAR通过基于专家混合的融合专家模块，学习将视觉特征与任务感知的语言表示对齐，使其能够在多样化的诊断任务中生成上下文相关的答案。通过将番茄病害分析表述为生成式视觉问答（VQA）任务，SOLAR提供了一个灵活的框架，支持在单一模型内进行多任务推理，同时提升性能并促进跨任务知识共享。我们在$41,677$张图像上评估SOLAR，其中包括$216,209$个问答（QA）对，以在封闭式和开放式问答设置下理解番茄叶片病害。实验结果表明，SOLAR在所有任务上均持续优于最先进的纯视觉、视觉语言和任务专用模型，展现出更优的准确性、鲁棒性和多模态推理能力。这些发现凸显了生成式多模态建模作为理解植物病害的有效方向的潜力。本研究的代码可在https:\u002F\u002Fgithub.com\u002FEnalisUs\u002FSOLAR获取。","arXiv (Cornell University)","2026-09-17T00:00:00Z",79,{"impact":110,"substance":337,"depth":110,"authority":386,"freshness":54,"relevant":21,"comment":387},13,"提出面向番茄病害的多模态生成式VQA模型SOLAR，在4万余张图像上验证多任务诊断性能，方法新颖、数据规模可观，对智慧植保有参考价值。",[389],{"name":382,"url":379},[26,27,29,391,392],"植物病害识别","多模态模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":396},{"candidates":397,"agricultural_evidence":46},[398,402,406],{"code":32,"score":45,"supported":46,"title":399,"abstract":400,"tags":401},[48],[50,48],[29],{"code":53,"score":137,"supported":46,"title":403,"abstract":404,"tags":405},[],[28,57],[26],{"code":60,"score":407,"supported":46,"title":408,"abstract":409,"tags":410},5,[],[256],[27,391],[412,413],"SOLAR 番茄病害 多模态模型","番茄叶片病害 视觉问答","SOLAR番茄病害多模态模型-2962","10.48550\u002Farxiv.2609.19555",{"doi":415,"openalex_id":417,"authors":418,"venue":382,"cited_by_count":86,"oa_url":379,"card":429,"direction":92,"ingested_from":95},"W7213586905",[419,422,424,426],{"name":420,"orcid":421},"Khang Nguyen Quoc","https:\u002F\u002Forcid.org\u002F0000-0003-4927-4822",{"name":423,"orcid":9},"Minh-Phuoc Tran",{"name":425,"orcid":9},"Gia-Han Truong",{"name":427,"orcid":428},"Luyl-Da Quach","https:\u002F\u002Forcid.org\u002F0000-0002-5661-4250",{"tldr":430,"method":431,"finding":432,"direction":92,"opportunity":433},"提出多模态生成模型SOLAR，统一理解番茄病害叶片的六类问答任务。","基于混合专家融合模块对齐视觉与任务感知语言，用4万余图像和21万问答对训练。","SOLAR在闭集和开放问答中均超越视觉、视觉语言及任务专用模型，展现更强推理能力。","可探索将生成式多模态VQA扩展到更多作物和田间实时场景，并融合传感器数据。","2026-09-19T23:30:55.245800Z",{"id":436,"title":437,"url":438,"summary":439,"summary_zh":440,"content":9,"source_name":441,"source_url":438,"published_at":442,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":234,"score_detail":443,"sources":445,"tags":447,"primary_topic":449,"secondary_topics":450,"classification":456,"search_phrases":478,"slug":481,"view_count":61,"doi":482,"paper":483,"created_at":495},2048,"Optimized Inception-v4 CNN Combined with YOLOv8 andXGBoost for Tomato Plant Disease Recognition and Classification","https:\u002F\u002Fdoi.org\u002F10.38094\u002Fjastt711230","This study proposes an integrated hybrid pipeline approach which is the combination of an optimized Inception-v4 Convolutional Neural Network (CNN), YOLOv8 (You Only Look Once version 8) and extreme gradient boosting (XG Boost) models to detect and classify Tomato plant diseases effectively and accurately. The proposed methodology is based on the fine-grained localization ability of the YOLOv8 model to accurately localize the affected area of the leaf, multi-scale deep feature extraction ability of the optimized Inceptionv4 CNN, and XG Boost to reduce dimensions and optimize features. The hybrid model combines the Inception-v4 CNN, YOLOv8, and XG Boost models with an average CPU processing time of about 200 milliseconds per image, the model performs better in terms of computational efficiency, robustness, and faster prediction. Experimental tests on Plant Village show that the model outperforms the state of the art in 10 disease categories. Specifically, the integrated hybrid system achieved high precision, recall, F1-score, mean Average Precision (m AP), and its training accuracy was more than 0.98 (up to 0.9969), and the testing accuracy was more than 0.96 (up to 0.9695). The hybrid framework shows good reliability even for diseases that are visually similar, such as bacterial spot, early blight, late blight, mosaic virus and yellow leaf curl virus. The optimization of batch size to 32, along with the tuning of the learning rate further improved the stability of training, convergence speed and the generalization of the overall model. The proposed system holds promise for real-time, scalable, and sustainable precision agriculture, aiming for early disease detection and yield protection. Future work includes incorporating Internet of Things (IoT) edge devices, in field environmental monitoring, multimodal data integration and applying Explainable Artificial Intelligence (EAI) techniques to enhance model interpretability for end-users.","本研究提出了一种集成的混合流水线方法，将优化的Inception-v4卷积神经网络（CNN）、YOLOv8（You Only Look Once version 8）和极端梯度提升（XG Boost）模型相结合，以有效且准确地检测和分类番茄植株病害。所提方法基于YOLOv8模型的细粒度定位能力来准确定位叶片受感染区域、优化的Inception-v4 CNN的多尺度深层特征提取能力，以及XG Boost用于降维和特征优化。该混合模型将Inception-v4 CNN、YOLOv8和XG Boost模型结合在一起，每幅图像的平均CPU处理时间约为200毫秒，模型在计算效率、鲁棒性和更快预测方面表现更优。在Plant Village上的实验测试表明，该模型在10个病害类别上优于现有最先进方法。具体而言，该集成混合系统实现了较高的精确率、召回率、F1分数、平均精度均值（mAP），其训练准确率超过0.98（最高达0.9969），测试准确率超过0.96（最高达0.9695）。即使对于视觉上相似的病害，如细菌性斑点病、早疫病、晚疫病、花叶病毒和黄花叶卷曲病毒，该混合框架也表现出良好的可靠性。将批量大小优化为32，并调整学习率，进一步提高了训练稳定性、收敛速度和整体模型的泛化能力。所提系统有望用于实时、可扩展和可持续的精准农业，旨在实现早期病害检测和产量保护。未来工作包括整合物联网（IoT）边缘设备、田间环境监测、多模态数据融合，以及应用可解释人工智能（EAI）技术以增强模型对最终用户的可解释性。","Journal of Applied Science and Technology Trends","2026-09-09T00:00:00Z",{"impact":111,"substance":18,"depth":110,"authority":17,"freshness":20,"relevant":21,"comment":444},"提出YOLOv8+Inception-v4+XGBoost混合模型用于番茄病害识别，准确率与效率数据扎实，对农业AI病害检测有参考价值。",[446],{"name":441,"url":438},[26,27,240,28,448],"番茄种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[451],{"code":452,"slug":453,"name":454,"path":455,"parent_code":123},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":457},{"candidates":458,"agricultural_evidence":46},[459,463,470,474],{"code":32,"score":45,"supported":46,"title":460,"abstract":461,"tags":462},[48],[50,48],[448],{"code":452,"score":20,"supported":46,"title":464,"abstract":465,"tags":469},[],[466,467,468],"物联网","internet of things","IoT",[],{"code":53,"score":54,"supported":46,"title":471,"abstract":472,"tags":473},[],[28,57],[26,28],{"code":60,"score":407,"supported":46,"title":475,"abstract":476,"tags":477},[],[256],[27,240],[479,480],"农业人工智能 病虫害识别 智慧农业 番茄种植","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番茄种植-2048","10.38094\u002Fjastt711230",{"doi":482,"openalex_id":484,"authors":485,"venue":441,"cited_by_count":86,"oa_url":438,"card":490,"direction":94,"ingested_from":95},"W7212078878",[486,488],{"name":487,"orcid":9},"Sowmya B",{"name":489,"orcid":9},"Guruprasad S",{"tldr":491,"method":492,"finding":493,"direction":92,"opportunity":494},"提出YOLOv8+Inception-v4+XGBoost混合模型，实现番茄叶片病害精准检测与分类。","YOLOv8定位病斑，Inception-v4提取多尺度特征，XGBoost降维","10类病害识别精度超现有方法，训练准确率0.9969，测试0.9695，单图CPU约200毫秒。","可探索IoT边缘部署、多模态数据融合与可解释AI，提升田间实时病害诊断可信度。","2026-09-10T23:30:14.735468Z"]