[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2039":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":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":49},2039,"An Adaptive AI-DSS Framework for Real-Time Image Analysis and Decision-Making in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.14445\u002F22315381\u002Fijett-v74i8p136","Environmental variability, pest infestation, and resource inefficiency have become increasingly problematic to agriculture, and on-line decision making using intelligent, adaptive technologies has been called for. This research is suggesting a novel Integrated Artificial Intelligence Decision Support System (AI-DSS) in precision agriculture so as to attain proper context-aware analysis and recommendation across dynamic field conditions. The framework is based on lightweight deep learning frameworks (MobileNetV2, Efficient Net-lite) and a newly designed Adaptive Feature Optimization (AFO) engine, which dynamically reweights convolutional features based on the temporal stability and environment consistency. Mathematically, the AFO mechanism is obtained via a weighted pooling of the instantaneous features of CNN with the temporal averaged prototypes using adaptive attention weights, which filter out transient noises due to illumination variations, occlusion, and sensor noises. The optimized features are fused with data from environmental and soil sensors in a hybrid Decision Support System (DSS) based on rule-based reasoning, Bayesian inference, and temporal tracking to construct explainable and region-specific recommendations to farmers. Experimental evaluations using the experimental data PlantVillage, DeepWeeds, and Fieldstream Sim demonstrate the superiority of the proposed AFO enhanced framework over the baseline CNN classifier in terms of classification accuracy (.95), macro F1 score (.95), and robustness to distortions (.15% improvement). Additionally, when deployed on edge devices like Raspberry Pi 4 and Nvidia Jetson Nano, the system achieves real-time inference latency (\u003C200 ms) and low energy consumption (~520 mJ\u002Fframe), which validates the scalability of the system in low-resource settings. In addition, the expert agreement was enhanced with the DSS module integration to 91.4% with 57% less false alarms. The obtained results validate the proposed AI-DSS framework with AFO as a promising solution to cover the distance between accuracy in a controlled lab environment and reliability in the field, providing a strong, explainable, and resource-efficient solution to the digital sustainable agriculture problem. This solution is further being expanded into proactive farm intelligence and climate resilience through multimodal sensing, satellite assisted crops monitoring, and adaptive decision-making-led solutions.","环境变异、病虫害侵袭和资源低效问题对农业的影响日益严重，亟需利用智能自适应技术进行在线决策。本研究提出了一种用于精准农业的新型集成人工智能决策支持系统（AI-DSS），旨在实现动态田间条件下的情境感知分析与推荐。该框架基于轻量级深度学习框架（MobileNetV2、EfficientNet-lite）和新设计的自适应特征优化（AFO）引擎，后者根据时间稳定性和环境一致性对卷积特征进行动态重加权。在数学上，AFO机制通过自适应注意力权重将CNN的瞬时特征与时间平均原型进行加权池化，从而滤除由光照变化、遮挡和传感器噪声引起的瞬态噪声。优化后的特征与环境和土壤传感器数据在混合决策支持系统（DSS）中融合，该系统基于规则推理、贝叶斯推断和时间追踪，为农民构建可解释的、区域特定的推荐方案。使用PlantVillage、DeepWeeds和Fieldstream Sim实验数据的评估表明，所提出的AFO增强框架在分类准确率（.95）、宏F1分数（.95）和抗失真鲁棒性（提升.15%）方面均优于基线CNN分类器。此外，在Raspberry Pi 4和Nvidia Jetson Nano等边缘设备上部署时，系统实现了实时推理延迟（\u003C200 ms）和低能耗（约520 mJ\u002F帧），验证了该系统在低资源环境下的可扩展性。同时，集成DSS模块后专家一致率提升至91.4%，误报率降低57%。所得结果验证了所提出的带有AFO的AI-DSS框架是一种有前景的解决方案，能够弥合受控实验室环境中的准确性与田间可靠性之间的差距，为数字可持续农业问题提供了稳健、可解释且资源高效的解决方案。该方案正进一步通过多模态传感、卫星辅助作物监测和自适应决策驱动的解决方案，扩展至主动式农场智能和气候韧性领域。",null,"International Journal of Engineering Trends and Technology","2026-09-09T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,12,9,1,"提出融合自适应特征优化与多源传感器推理的轻量级AI决策支持框架，在多个公开数据集与边缘设备上验证了精度与实时性，方法新颖、结论可靠，对智慧农业落地有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","边缘计算","决策支持系统","精准农业",0,"10.14445\u002F22315381\u002Fijett-v74i8p136",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":40,"card":41,"direction":47,"ingested_from":48},"W7212021859",[36,38],{"name":37,"orcid":9},"Yebhushi Prashanth",{"name":39,"orcid":9},"Dr.Manna SheelaRani Chetty","https:\u002F\u002Fijettjournal.org\u002FVolume-74\u002FIssue-8\u002FIJETT-V74I8P136.pdf",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"提出自适应AI决策支持系统，用轻量网络与特征优化实现精准农业实时图像分析与决策。","MobileNetV2\u002FEfficientNet-lite加自适应特征优化引擎，","分类准确率与宏F1达0.95，边缘设备延迟低于200毫秒，专家一致率91.4%。","农业人工智能与决策模型","可探索多模态传感与卫星遥感融合，提升田间跨区域泛化与气候韧性决策能力。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:09.239423Z"]