Kisan AI让农事建议兼顾收益，帮农民种得对、卖得好。
Smart Profit-Aware Crop Advisory System: Kisan AI
- 在农艺参数中加入市场价格，用随机森林模型预测最优作物
- 模型准确率达99.3%，市场价特征显著提升决策经济性
- 集成多语言聊天机器人与价格/病害预测，适配印度农户使用
现代作物咨询系统存在关键缺陷——经济盲视，主要优化生物产量而忽视市场价格，可能导致农艺上合理但财务上亏损的决策。本文提出Kisan AI，一种智能盈利感知的作物咨询系统，通过全栈式研究实现突破。我们在九特征基准数据集上训练随机森林（RF）分类器，标准七项农艺属性外新增市场价变量，并与八种基线模型对比，评估指标包括准确率、精确率、召回率、F1分数和对数损失。结果表明，RF模型达到99.3%的最高准确率和最低对数损失，证实市场价作为预测特征具有有效性和重要性。随后，将该模型嵌入多语言渐进式Web应用，结合Facebook Prophet六月价格预测引擎与MobileNetV2病害检测模块，并由九语种基于Anthropic Claude API的AI聊天机器人统一整合，形成可移动端安装的综合平台，服务印度各地农民。
原文摘要 · Abstract (English)
Modern crop advisory systems exhibit a critical limitation termed \textit{economic blindness}. These systems primarily optimize for biological yield, often overlooking market price, which can lead farmers toward agronomically sound yet financially unviable decisions. In this paper, we develop Kisan AI, a smart profit-aware crop advisory system that resolves the above-mentioned limitation through a research-driven, full-stack application. We train the Random Forest(RF) classifier model on a nine-feature benchmark dataset, the standard seven agronomic attributes augmented with a \textit{market\_price} variable, and evaluated against eight baseline models, considering the evaluation matrices, such as, accuracy, precision, recall, F1-score, and Log Loss. The RF model achieves the highest accuracy of 99.3\% and the lowest Log Loss, confirming that the inclusion of market price as a predictive feature is both valid and impactful. We then implement the RF model within a multilingual progressive Web App alongside a Facebook Prophet six-month price forecasting engine and a MobileNetV2 disease detection module. A nine-language AI chatbot powered by the Anthropic Claude API unifies all modules into a single, mobile-installable platform accessible to farmers across India.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。