[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2036":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":49},2036,"Smart crop recommendation: fusing nutrient and climate data with Krill Herd Optimization and explainable AI","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1910196","Crop recommendation is a vital part of precision agriculture as it helps farmers choose appropriate crops according to the nutrient profile and environmental conditions. This paper presents a crop recommendation framework in which Multi-Layer Perceptron (MLP), XGBoost, and Tab Transformer are first evaluated as baseline prediction models, followed by the proposed Krill Herd Optimization (KHO)-based explainable framework integrated with Explainable Artificial Intelligence (XAI). These eleven parameters are created based on agronomic and environmental aspects, namely: Nitrogen, Phosphorus, Potassium, Copper, Iron, Magnesium, Sulphur, Temperature, Rainfall, pH and Humidity. For better model transparency and to aid informed decision-making, model explanations with SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are used to identify feature contributions to crop predictions both locally and globally. The experimental results showed that Tab Transformer significantly outperformed the other models, with an accuracy of 0.99, precision of 0.98, recall of 0.99 and F1-score of 0.98. The proposed framework further incorporates Krill Herd Optimization (KHO) to generate optimized nutrient and climate profiles, while Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) are used for comparative evaluation of optimization performance. By combining explainable AI with optimization methods, the framework improves crop suitability prediction and provides transparent insights into the factors influencing crop recommendations, ensuring reliable decision support for practical farming applications. The proposed framework supports precision agriculture by enabling data-driven crop selection, reducing unnecessary fertilizer usage, optimizing crop productivity, and promoting sustainable farming practices.","作物推荐是精准农业的重要组成部分，有助于农民根据养分状况和环境条件选择适宜的作物。本文提出了一个作物推荐框架，首先评估多层感知机（MLP）、XGBoost和Tab Transformer作为基线预测模型，随后提出基于磷虾群优化（KHO）的可解释框架，并集成可解释人工智能（XAI）。基于农艺和环境因素构建了十一个参数，即：氮、磷、钾、铜、铁、镁、硫、温度、降雨量、pH值和湿度。为提高模型透明度并辅助知情决策，采用SHAP（SHapley加性解释）和LIME（局部可解释模型无关解释）进行模型解释，以在局部和全局层面识别特征对作物预测的贡献。实验结果表明，Tab Transformer显著优于其他模型，准确率为0.99，精确率为0.98，召回率为0.99，F1分数为0.98。所提框架进一步引入磷虾群优化（KHO）以生成优化的养分和气候方案，同时使用遗传算法（GA）、粒子群优化（PSO）和模拟退火（SA）对优化性能进行比较评估。通过将可解释人工智能与优化方法相结合，该框架提高了作物适宜性预测能力，并提供了影响作物推荐因素的透明洞察，确保为实际农业应用提供可靠的决策支持。所提框架通过实现数据驱动的作物选择、减少不必要的肥料使用、优化作物生产力并促进可持续农业实践，为精准农业提供支持。",null,"Frontiers in Artificial Intelligence","2026-09-09T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,13,9,1,"融合营养与气候数据、Krill Herd优化与可解释AI的作物推荐框架，方法新颖、结论可靠，对精准农业决策有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","精准施肥","可解释AI","作物推荐",0,"10.3389\u002Ffrai.2026.1910196",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":41,"direction":47,"ingested_from":48},"W7211994206",[37,39],{"name":38,"orcid":9},"P. Latha",{"name":40,"orcid":9},"P. Kumaresan",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"提出融合养分与气候数据、KHO优化和可解释AI的作物推荐框架，Tab Transformer精度达0","用MLP、XGBoost、Tab Transformer建模，结合KHO优化与S","Tab Transformer预测最优（准确率0.99），KHO优化养分气候方案，XAI提升推荐透明","农业人工智能与决策模型","可探索将可解释AI与优化算法嵌入真实农田物联网实时数据流，验证跨区域泛化与农户采纳效果。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:09.054372Z"]