[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3605":3,"related-3605":59},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":58},3605,"AquaCrop-derived surrogate model for crop yield response to monthly irrigation deficit","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104995","Context: The Upper Yellow River Basin faces acute water competition between cascade hydropower operations and large irrigation districts. Traditional multi-objective reservoir operation leads to distorted energy-food benefit trade-offs from oversimplified linear representation of agricultural objectives, while process-based crop models are infeasible for direct embedding into reservoir operation frameworks requiring long-sequence iterative optimization due to excessive computational cost. Objective: This study aims to develop an AquaCrop-derived and computationally efficient agricultural surrogate model that accurately maps monthly water deficits to nonlinear crop yield responses, thereby providing a refined agricultural loss function for reservoir operation frameworks. Methods: Focusing on spring wheat, spring maize, and sunflower in the Hetao Irrigation District, a locally calibrated AquaCrop model was applied to simulate yield responses under full-gradient deficit irrigation scenarios. On this basis, we developed high-order polynomial ridge regression surrogate models driven by growing-season Standardized Precipitation Evapotranspiration Index and monthly irrigation deficit ratios. Finally, the model performance was validated using an offline dual validation framework covering generalization performance and decision consistency, and critical irrigation months and yield-loss thresholds were further quantified. Results and conclusions: The surrogate model achieved a global coefficient of determination above 0.93 across dual held-out test sets, with median yield-loss deviations from the sample-best AquaCrop scheme of less than 2%, and outperformed benchmark irrigation allocation schemes. However, prediction accuracy declined substantially under severe water stress, especially for spring maize. Furthermore, computational efficiency was improved by over five orders of magnitude compared with the process-based model. Under the locally parameterized AquaCrop framework, this study identified April as the most irrigation-sensitive month for spring wheat, May for spring maize, and June for sunflower. Compared with interval median scheme, the surrogate model-recommended scheme delayed physically feasible yield-loss thresholds by 6.7 to 14.7 percentage points, with bootstrap 95% confidence intervals entirely above zero. Significance: This study highlights that the AquaCrop-derived surrogate model bridges the gap between crop modeling and reservoir operation by mapping monthly water-allocation decisions to nonlinear crop yield responses. Aligned with the monthly decision cycle of reservoir operation and with ultra-high computational efficiency, this model is expected to serve as a crucial methodological component for future multi-objective reservoir operation frameworks.","背景：黄河上游流域面临梯级水电运行与大型灌区之间尖锐的水资源竞争。传统多目标水库调度因农业目标的过度简化线性表征，导致能量-粮食效益权衡关系失真；而基于过程的作物模型由于计算成本过高，无法直接嵌入需要长序列迭代优化的水库调度框架。目标：本研究旨在开发一种源自AquaCrop且计算高效的农业代理模型，能够准确刻画月度水分亏缺与作物产量非线性响应之间的映射关系，从而为水库调度框架提供精细化的农业损失函数。方法：以河套灌区的春小麦、春玉米和向日葵为研究对象，采用经本地校准的AquaCrop模型模拟全梯度亏缺灌溉情景下的产量响应。在此基础上，构建了以生长季标准化降水蒸散指数和月度灌溉亏缺比为驱动因子的高阶多项式岭回归代理模型。最后，采用涵盖泛化性能和决策一致性的离线双重验证框架对模型性能进行验证，并进一步量化了关键灌溉月份和产量损失阈值。结果与结论：代理模型在两个独立测试集上的全局决定系数均超过0.93，与样本最优AquaCrop方案相比，产量损失偏差中位数低于2%，且优于基准灌溉配水方案。然而，在重度水分胁迫条件下，预测精度显著下降，春玉米尤为明显。此外，与基于过程的模型相比，计算效率提升了五个数量级以上。在本地参数化的AquaCrop框架下，本研究确定春小麦对灌溉最敏感的月份为4月，春玉米为5月，向日葵为6月。与区间中值方案相比，代理模型推荐方案将物理可行的产量损失阈值推迟了6.7至14.7个百分点，Bootstrap 95%置信区间完全位于零以上。意义：本研究突出表明，源自AquaCrop的代理模型通过映射……弥合了作物建模与水库调度之间的差距。",null,"Agricultural Systems","2026-09-26T00:00:00Z","论文",10,true,82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,23,14,9,1,"将AquaCrop过程模型转化为高效代理模型，为水库调度提供非线性农业损失函数，方法新颖、验证扎实，对灌区水资源协同管理有实用价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","作物模型","节水灌溉","河套灌区",[32,33],"河套灌区 春小麦 灌溉亏缺","AquaCrop 代理模型 产量响应","河套灌区春小麦灌溉亏缺-3605",0,"10.1016\u002Fj.agsy.2026.104995",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":51,"direction":55,"ingested_from":57},"W7214449839",[40,42,45,47,49],{"name":41,"orcid":9},"任泽岭",{"name":43,"orcid":44},"Binquan Li","https:\u002F\u002Forcid.org\u002F0000-0002-9958-0396",{"name":46,"orcid":9},"Jing Liu",{"name":48,"orcid":9},"Kuang Li",{"name":50,"orcid":9},"Zhongmin Liang",{"tldr":52,"method":53,"finding":54,"direction":55,"opportunity":56},"构建AquaCrop衍生的代理模型，快速映射月度灌溉亏缺与作物产量的非线性响应。","局部校准AquaCrop模拟亏缺灌溉，用SPEI与亏缺比驱动高阶多项式岭回归代理","代理模型R²>0.93、计算效率提升五个数量级，但重旱下春玉米精度明显下降。","农业人工智能与决策模型","可探索极端干旱下代理模型精度衰减的校正方法，并耦合水库调度实现能量-粮食协同优化。","openalex","2026-09-27T23:30:06.912579Z",{"total":60,"page":21,"page_size":60,"items":61},6,[62,114,171,201,255,307],{"id":63,"title":64,"url":65,"summary":66,"summary_zh":9,"content":9,"source_name":10,"source_url":65,"published_at":67,"category":12,"cover_url":9,"hotness":13,"is_selected":68,"score":69,"score_detail":70,"sources":74,"tags":76,"search_phrases":79,"slug":82,"view_count":21,"doi":83,"paper":84,"created_at":113},2622,"Synergizing process-based modeling and data-driven learning for precision nitrogen optimization in winter wheat","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104983","Synergizing process-based modeling and data-driven learning for precision nitrogen optimization in winter wheat。Agricultural Systems","2026-09-16T00:00:00Z",false,79,{"impact":17,"substance":71,"depth":72,"authority":19,"freshness":13,"relevant":21,"comment":73},20,17,"将过程模型与数据驱动学习融合用于冬小麦精准氮肥优化，方法新颖、发表于核心期刊且时效性强，具备进入每日精选的价值。",[75],{"name":10,"url":65},[26,27,77,28,78],"精准施肥","冬小麦",[80,81],"农业人工智能 作物模型 智慧农业 精准施肥","农业人工智能 作物模型","农业人工智能作物模型智慧农业精准施肥-2622","10.1016\u002Fj.agsy.2026.104983",{"doi":83,"openalex_id":85,"authors":86,"venue":10,"cited_by_count":35,"oa_url":65,"card":9,"direction":9,"ingested_from":57},"W7213352238",[87,89,92,95,97,99,101,103,105,107,109,111],{"name":88,"orcid":9},"Yuru Ye",{"name":90,"orcid":91},"Qian Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0750-7843",{"name":93,"orcid":94},"Davide Cammarano","https:\u002F\u002Forcid.org\u002F0000-0003-0918-550X",{"name":96,"orcid":9},"Kang Yu",{"name":98,"orcid":9},"Siva K. Balasundram",{"name":100,"orcid":9},"Wei Li",{"name":102,"orcid":9},"Xiuli Li",{"name":104,"orcid":9},"Xiaojun Liu",{"name":106,"orcid":9},"Yongchao Tian",{"name":108,"orcid":9},"Yan Zhu",{"name":110,"orcid":9},"Weixing Cao",{"name":112,"orcid":9},"Qiang Cao","2026-09-16T23:30:05.340754Z",{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":10,"source_url":117,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":68,"score":121,"score_detail":122,"sources":126,"tags":128,"search_phrases":131,"slug":134,"view_count":35,"doi":135,"paper":136,"created_at":170},2505,"A continually evolving knowledge-guided deep learning framework for daily maize yield formation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104981","CONTEXT As multi-source agricultural datasets expand, accurate and interpretable simulation of crop yield formation is increasingly feasible and important for food security. Process-based models are widely used for yield forecasting but require laborious regional calibration. Artificial intelligence models learn physiological patterns from large datasets but lack physiological consistency and generalize poorly under data-scarce conditions. OBJECTIVE We developed AgroEvoDeep-Yield, a knowledge-guided model that encodes mechanistic understanding through its neural architecture, enabling explicit and interpretable simulation of daily maize yield formation from flowering to maturity. We further established a framework that supports continual model evolution as new data become available. METHODS The model was pretrained on APSIM-simulated grain number and daily grain biomass sequences to impose physiological consistency. Following pretraining, the model evolved through two continual learning stages. First, field-level continual learning using 796 field observations reduced biases inherited from APSIM. The model was then adapted to regional-scale multi-source inputs using long-term records from 945 meteorological stations and expert-constrained cultivar-parameter optimization. RESULTS AND CONCLUSIONS After field-level continual learning, AgroEvoDeep-Yield reduced grain number and yield simulation errors by 29% and 24%, respectively, relative to APSIM, and outperformed conventional machine learning models in out-of-region prediction. Region-level continual learning reduced yield RMSE to 1.09 t\u002Fha, and expert-constrained cultivar-parameter optimization further reduced it to 0.80 t\u002Fha. SIGNIFICANCE This study demonstrates how a knowledge-guided model can be repeatedly adapted as new data become available, providing a practical framework for continual model evolution and a basis for future agricultural decision support.","背景 随着多源农业数据集的不断扩展，准确且可解释地模拟作物产量形成过程日益可行，对粮食安全也愈发重要。基于过程的模型广泛用于产量预测，但需要耗费大量精力进行区域校准。人工智能模型能够从大规模数据集中学习生理模式，但缺乏生理一致性，在数据稀缺条件下泛化能力较差。目标 我们开发了AgroEvoDeep-Yield，这是一种知识引导模型，通过其神经网络架构编码机理认识，能够对玉米从开花到成熟的逐日产量形成过程进行显式且可解释的模拟。我们还建立了一个框架，以支持在新数据可用时模型的持续演化。方法 该模型在APSIM模拟的粒数和逐日籽粒生物量序列上进行预训练，以施加生理一致性约束。预训练后，模型通过两个持续学习阶段进行演化。首先，利用796个田间观测数据进行田块级持续学习，以减少继承自APSIM的偏差。随后，利用945个气象站的长期记录和专家约束的品种参数优化，将模型适配到区域尺度多源输入。结果与结论 经过田块级持续学习后，AgroEvoDeep-Yield相对于APSIM将粒数和产量模拟误差分别降低了29%和24%，并在区域外预测中优于传统机器学习模型。区域级持续学习将产量RMSE降至1.09 t\u002Fha，专家约束的品种参数优化进一步将其降至0.80 t\u002Fha。意义 本研究表明，知识引导模型可以在新数据可用时被反复适配，为模型持续演化提供了实用框架，并为未来农业决策支持奠定了基础。","2026-09-14T00:00:00Z",85,{"impact":123,"substance":18,"depth":17,"authority":19,"freshness":124,"relevant":21,"comment":125},22,8,"知识引导的深度学习框架实现玉米逐日产量形成模拟，误差显著低于APSIM且支持持续学习，方法新颖、数据规模扎实，对智慧农业决策支持有参考价值。",[127],{"name":10,"url":117},[26,27,129,130,28],"产量预测","玉米",[132,133],"农业人工智能 产量预测 作物模型 智慧农业","农业人工智能 产量预测","农业人工智能产量预测作物模型智慧农业-2505","10.1016\u002Fj.agsy.2026.104981",{"doi":135,"openalex_id":137,"authors":138,"venue":10,"cited_by_count":35,"oa_url":117,"card":165,"direction":55,"ingested_from":57},"W7212532256",[139,141,143,146,149,152,154,157,159,162],{"name":140,"orcid":9},"Junji Ou",{"name":142,"orcid":9},"Wenyao Yan",{"name":144,"orcid":145},"Fangzheng Chen","https:\u002F\u002Forcid.org\u002F0009-0006-5061-6227",{"name":147,"orcid":148},"Tao Ye","https:\u002F\u002Forcid.org\u002F0000-0002-5037-8410",{"name":150,"orcid":151},"Ke Liu","https:\u002F\u002Forcid.org\u002F0000-0002-8343-0449",{"name":153,"orcid":9},"Matthew Tom Harrison",{"name":155,"orcid":156},"William D. Batchelor","https:\u002F\u002Forcid.org\u002F0000-0002-3881-6246",{"name":158,"orcid":9},"Yong Chen",{"name":160,"orcid":161},"Kelin Hu","https:\u002F\u002Forcid.org\u002F0000-0001-9321-0821",{"name":163,"orcid":164},"Puyu Feng","https:\u002F\u002Forcid.org\u002F0000-0003-4845-9876",{"tldr":166,"method":167,"finding":168,"direction":55,"opportunity":169},"提出知识引导的深度学习框架AgroEvoDeep-Yield，实现玉米逐日产量形成的可解释模拟与持续","基于APSIM模拟数据预训练，结合796个田间观测和945个气象站数据持续学习。","田间持续学习使粒数和产量误差比APSIM降低29%和24%，区域优化后RMSE降至0.80 t\u002Fha","可探索将知识引导与持续学习框架迁移至其他作物，并融合实时遥感与物联网数据实现动态产量预测。","2026-09-15T23:30:04.611750Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":9,"content":9,"source_name":176,"source_url":9,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":68,"score":178,"score_detail":179,"sources":182,"tags":184,"search_phrases":187,"slug":190,"view_count":35,"doi":9,"paper":191,"created_at":200},2395,"[预印本]Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.06740","arXiv 2609.06740（2026-09-10）。基于海南智慧农业平台两项蒙特卡洛实验：实验1显示AI模式下20%农药减量概率从近零升至20.7%（基线）至49%（诊断精度0.95、采纳率0.85），15%化肥减量概率从近零升至52.0%；实验2显示AI灌溉调度增加5.0个百分点节水至16.0%，稻田CH4减排30.5%。研究指出农户采纳是主要瓶颈。","arXiv | 2026-09-10","2026-09-10T00:00:00Z",78,{"impact":17,"substance":123,"depth":17,"authority":180,"freshness":124,"relevant":21,"comment":181},12,"预印本以蒙特卡洛模拟量化AI模块在农药化肥减量与稻田减排上的边际绿色贡献，数据与结论有新意，但尚待同行评议，属细分领域前沿进展。",[183],{"name":176,"url":174},[26,27,185,29,186],"精准农业","农业减排",[188,189],"农业人工智能 农业减排 智慧农业 精准农业","农业人工智能 农业减排","农业人工智能农业减排智慧农业精准农业-2395",{"doi":9,"openalex_id":9,"authors":192,"venue":9,"cited_by_count":35,"oa_url":9,"card":193,"direction":197,"ingested_from":199},[],{"tldr":194,"method":195,"finding":196,"direction":197,"opportunity":198},"用蒙特卡洛实验模拟智慧农业平台中AI模块对农药化肥减量与节水减排的边际绿色贡献。","海南智慧农业平台数据，两项蒙特卡洛实验，含诊断精度与采纳率情景。","AI使农药减量20%概率升至20.7%-49%，化肥减量15%概率达52%，稻田CH4减排30.5%","农业绿色发展与碳","可将农户采纳行为内生化，研究采纳率提升机制与AI模块绿色效益的经济激励设计。","agent","2026-09-14T00:06:32.920282Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":9,"source_name":207,"source_url":204,"published_at":208,"category":12,"cover_url":9,"hotness":13,"is_selected":68,"score":209,"score_detail":210,"sources":214,"tags":216,"search_phrases":218,"slug":220,"view_count":21,"doi":221,"paper":222,"created_at":254},1596,"AgriPINN: A Process-Informed Neural Network for Interpretable and Scalable Crop Biomass Prediction under Water Stress","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102539","Accurate estimation of crop above-ground dry biomass (AGB) under water stress is important for assessing agricultural production, managing irrigation, and supporting water-security-oriented decision-making. Data-driven models enable efficient large-scale prediction but often provide limited physiological interpretation and can be sensitive to domain shifts, whereas process-based crop models require extensive calibration and detailed management inputs. We propose AgriPINN, a process-informed neural network that embeds a differentiable biomass-growth equation derived from LINTUL5 into neural-network training. AGB is the sole supervised target. The network simultaneously estimates leaf area index (LAI), photosynthetically active radiation (PAR), radiation-use efficiency (RUE), and the water-stress factor ( F W ); their predicted values enter the process residual, whereas the corresponding field measurements are held out from optimisation and used only for post hoc evaluation. AgriPINN is pretrained on 65 years of SIMPLACE simulations covering 397 German NUTS-3 regions and fine-tuned on three years of wheat and maize field experiments under sheltered, rainfed, and irrigated treatments. On the main benchmark, AgriPINN reduces RMSE by 29.7% for winter wheat and 22.8% for maize relative to CNN–Transformer. Relative to the strongest non-AgriPINN baseline for each crop, the corresponding reductions are 9.0% and 2.2%. Additional evaluations include four representative neural backbones, sensitivity analysis of the process-loss weight, an ablation without pretraining, and perturbation tests on simulated labels. The transferability demonstrated in this study is limited to the independent datasets and the German temperate agro-ecological setting evaluated here.","水分胁迫下作物地上部干生物量（AGB）的准确估算对于评估农业生产、管理灌溉以及支持以水安全为导向的决策至关重要。数据驱动模型能够实现高效的大尺度预测，但往往提供的生理学解释有限，且可能对领域偏移敏感；而基于过程的作物模型则需要大量校准和详细的管理输入。我们提出AgriPINN，一种过程信息神经网络，将源自LINTUL5的可微生物量增长方程嵌入神经网络训练中。AGB是唯一的监督目标。该网络同时估算叶面积指数（LAI）、光合有效辐射（PAR）、辐射利用效率（RUE）以及水分胁迫因子（F W ）；其预测值进入过程残差，而相应的田间实测值则从优化中排除，仅用于事后评估。AgriPINN在覆盖德国397个NUTS-3区域的65年SIMPLACE模拟数据上进行了预训练，并在三年遮雨、雨养和灌溉处理下的小麦和玉米田间试验数据上进行了微调。在主要基准测试中，相对于CNN–Transformer，AgriPINN使冬小麦的RMSE降低了29.7%，玉米降低了22.8%。相对于每种作物最强的非AgriPINN基线，相应的降幅分别为9.0%和2.2%。额外评估包括四种代表性神经骨干网络、过程损失权重的敏感性分析、无预训练的消融实验以及模拟标签的扰动测试。本研究所展示的可迁移性仅限于所评估的独立数据集和德国温带农业生态情境。","Smart Agricultural Technology","2026-09-01T00:00:00Z",75,{"impact":17,"substance":123,"depth":211,"authority":180,"freshness":212,"relevant":21,"comment":213},19,4,"提出过程信息神经网络AgriPINN，结合机理模型与深度学习，显著提升水分胁迫下作物生物量预测精度，方法新颖且数据扎实。",[215],{"name":207,"url":204},[26,27,28,217],"遥感估产",[219,81],"农业人工智能 作物模型 智慧农业 遥感估产","农业人工智能作物模型智慧农业遥感估产-1596","10.1016\u002Fj.atech.2026.102539",{"doi":221,"openalex_id":223,"authors":224,"venue":207,"cited_by_count":35,"oa_url":204,"card":249,"direction":55,"ingested_from":57},"W7125531933",[225,227,229,232,235,238,240,242,244,246],{"name":226,"orcid":9},"Yue Shi",{"name":228,"orcid":9},"Amit Kumar Srivastava",{"name":230,"orcid":231},"Dominik Behrend","https:\u002F\u002Forcid.org\u002F0009-0000-9081-7169",{"name":233,"orcid":234},"Thomas Gaiser","https:\u002F\u002Forcid.org\u002F0000-0002-5820-2364",{"name":236,"orcid":237},"Thuy Huu Nguyen","https:\u002F\u002Forcid.org\u002F0000-0003-3870-986X",{"name":239,"orcid":9},"Xin Zhang",{"name":241,"orcid":9},"Tam Sobeih",{"name":243,"orcid":9},"Frank Ewert",{"name":245,"orcid":9},"Krishnagopal Halder",{"name":247,"orcid":248},"Liangxiu Han","https:\u002F\u002Forcid.org\u002F0000-0003-2491-7473",{"tldr":250,"method":251,"finding":252,"direction":55,"opportunity":253},"提出过程信息神经网络AgriPINN，嵌入作物生长方程，提升水分胁迫下生物量预测精度与可解释性。","将LINTUL5生长方程嵌入神经网络训练，预训练于SIMPLACE模拟数据，微调","相比CNN-Transformer，冬小麦RMSE降低29.7%，玉米降低22.8%，且具有生理可解","可探索将过程信息神经网络扩展到其他作物、气候区或胁迫类型，并验证其跨域泛化能力。","2026-09-04T23:30:04.364676Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":260,"content":9,"source_name":261,"source_url":258,"published_at":262,"category":12,"cover_url":9,"hotness":13,"is_selected":68,"score":263,"score_detail":264,"sources":268,"tags":270,"search_phrases":272,"slug":274,"view_count":35,"doi":275,"paper":276,"created_at":306},1592,"A formal approach to crop modeling: Automatic code generation and grid simulation using finite state automata","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112367","A formal approach to crop modeling: Automatic code generation and grid simulation using finite state automata。Computers and Electronics in Agriculture","作物建模的形式化方法：基于有限状态自动机的自动代码生成与网格模拟。《计算机与电子在农业中的应用》","Computers and Electronics in Agriculture","2026-09-03T00:00:00Z",62,{"impact":180,"substance":17,"depth":265,"authority":19,"freshness":266,"relevant":21,"comment":267},16,2,"提出基于有限状态自动机的作物建模形式化方法，实现代码自动生成与网格模拟，方法新颖，对作物模型标准化有推动意义。",[269],{"name":261,"url":258},[26,27,28,271],"模型模拟",[273,81],"农业人工智能 作物模型 智慧农业 模型模拟","农业人工智能作物模型智慧农业模型模拟-1592","10.1016\u002Fj.compag.2026.112367",{"doi":275,"openalex_id":277,"authors":278,"venue":261,"cited_by_count":35,"oa_url":9,"card":301,"direction":55,"ingested_from":57},"W7207652938",[279,282,285,287,289,292,295,298],{"name":280,"orcid":281},"Elia Brentarolli","https:\u002F\u002Forcid.org\u002F0000-0002-9352-0069",{"name":283,"orcid":284},"Davide Quaglia","https:\u002F\u002Forcid.org\u002F0000-0002-0775-939X",{"name":286,"orcid":9},"Luca Benvenuti",{"name":288,"orcid":9},"Tiziano Villa",{"name":290,"orcid":291},"Daniele Massa","https:\u002F\u002Forcid.org\u002F0000-0003-3179-0415",{"name":293,"orcid":294},"P. Battista","https:\u002F\u002Forcid.org\u002F0000-0003-1858-9653",{"name":296,"orcid":297},"B. Rapi","https:\u002F\u002Forcid.org\u002F0000-0002-3089-5664",{"name":299,"orcid":300},"Luca Incrocci","https:\u002F\u002Forcid.org\u002F0000-0001-9994-763X",{"tldr":302,"method":303,"finding":304,"direction":55,"opportunity":305},"提出用有限状态自动机形式化作物模型，自动生成代码并模拟网格作物生长。","有限状态自动机建模，自动代码生成，网格模拟。","该方法能简化作物模型开发，提高模拟效率与可重用性。","可探索将有限状态机与机器学习结合，实现作物模型自适应参数调整与实时决策。","2026-09-04T23:30:01.711814Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":9,"content":9,"source_name":312,"source_url":310,"published_at":313,"category":12,"cover_url":9,"hotness":13,"is_selected":68,"score":314,"score_detail":315,"sources":318,"tags":320,"search_phrases":323,"slug":325,"view_count":21,"doi":326,"paper":327,"created_at":343},1222,"A general Sphero-Cylindrical discrete element model of sorghum for optimising header parameters to minimise harvest losses","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104548","A general Sphero-Cylindrical discrete element model of sorghum for optimising header parameters to minimise harvest losses。Biosystems Engineering","Biosystems Engineering","2026-08-26T00:00:00Z",65,{"impact":316,"substance":71,"depth":17,"authority":180,"freshness":35,"relevant":21,"comment":317},15,"提出高粱球柱离散元模型优化割台参数以减少收获损失，方法新颖，对农业机械智能化有参考价值，但发表于2026年8月，时效性低。",[319],{"name":312,"url":310},[26,27,28,321,322],"高粱","收获减损",[324,81],"农业人工智能 作物模型 收获减损 智慧农业","农业人工智能作物模型收获减损智慧农业-1222","10.1016\u002Fj.biosystemseng.2026.104548",{"doi":326,"openalex_id":328,"authors":329,"venue":312,"cited_by_count":35,"oa_url":9,"card":9,"direction":9,"ingested_from":57},"W7204250587",[330,333,336,338,341],{"name":331,"orcid":332},"Tao Liu","https:\u002F\u002Forcid.org\u002F0000-0003-4111-7961",{"name":334,"orcid":335},"Ning Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8533-395X",{"name":337,"orcid":9},"Huowang Liu",{"name":339,"orcid":340},"Zhengxin Xu","https:\u002F\u002Forcid.org\u002F0000-0002-4178-6789",{"name":342,"orcid":9},"Qingjie Wang","2026-09-01T04:03:07.167234Z"]