[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2338":3},{"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":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":48},2338,"An optimized machine learning approach for reliable agronomic parameter prediction in precision farming systems","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71592-1","Optimized machine learning models are very important for improving predictive performance in precision agriculture because they let us analyze soil and environmental data in a data-driven way. But conventional predictive methods often can’t be used to make generalizations because agronomic data is often very variable, has nonlinear interactions, and is very different from one another. The study presents an enhanced machine learning-based predictive framework for estimating agricultural parameters utilizing structured numerical soil and environmental datasets. The framework combines systematic data preprocessing, feature selection, and hyperparameter optimization to make models more stable and reliable. The model performance was evaluated using five-fold cross-validation and standard regression metrics, including the Coefficient of Determination (R 2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The average R 2 is 0.696 ± 0.148, the RMSE is 5.160 ± 1.571, and the MAE is 4.636 ± 1.545, which shows that the model can make accurate predictions across all validation folds. Further the classification of the growth stages of spinach is done with an accuracy of 84.63% and with the precision of 85% using the proposed Hybrid ensemble model. The results show that the proposed framework works well with nonlinear agricultural data and could be used for data-driven decisions in precision farming systems.","优化的机器学习模型对于提升精准农业中的预测性能至关重要，因为它使我们能够以数据驱动的方式分析土壤和环境数据。然而，传统的预测方法往往无法用于泛化，因为农艺数据通常变异性很大、具有非线性交互作用，且彼此之间差异显著。本研究提出了一种基于增强机器学习的预测框架，利用结构化数值土壤和环境数据集来估算农业参数。该框架结合了系统化的数据预处理、特征选择和超参数优化，使模型更加稳定可靠。采用五折交叉验证和标准回归指标评估模型性能，包括决定系数（R²）、均方根误差（RMSE）和平均绝对误差（MAE）。平均R²为0.696 ± 0.148，RMSE为5.160 ± 1.571，MAE为4.636 ± 1.545，表明该模型在所有验证折中均能做出准确预测。此外，利用所提出的混合集成模型对菠菜生长阶段进行分类，准确率达到84.63%，精确率达到85%。结果表明，所提出的框架能够很好地处理非线性农业数据，可用于精准农业系统中的数据驱动决策。",null,"Scientific Reports","2026-09-12T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,15,14,8,1,"该论文提出融合预处理、特征选择与超参数优化的机器学习框架，在土壤环境数据上取得R²约0.70的预测表现并以84.63%准确率识别菠菜生长期，方法扎实但属常规模型优化，产业影响有限，可作为智慧农业技术参考。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","机器学习","精准农业","土壤数据",0,"10.1038\u002Fs41598-026-71592-1",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":41,"direction":45,"ingested_from":47},"W7212396250",[37,39],{"name":38,"orcid":9},"T. Suba",{"name":40,"orcid":9},"K. Lakshmi Joshitha",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"提出优化机器学习框架，用土壤环境数据预测农艺参数并分类菠菜生长阶段。","数据预处理、特征选择、超参数优化，五折交叉验证与混合集成模型。","回归平均R²为0.696，菠菜生长阶段分类准确率达84.63%。","农业人工智能与决策模型","可探索跨作物、跨区域迁移学习与可解释性，提升非线性农艺数据泛化能力。","openalex","2026-09-13T23:30:43.564799Z"]