Smart Profit-Aware Crop Advisory System: Kisan AI
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910183120175104 |
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| author | Dwibedy, Debasis Nishtala, Avyay Mukku, Pranathi Snehaja, D |
| author_facet | Dwibedy, Debasis Nishtala, Avyay Mukku, Pranathi Snehaja, D |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_00133 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Smart Profit-Aware Crop Advisory System: Kisan AI Dwibedy, Debasis Nishtala, Avyay Mukku, Pranathi Snehaja, D Machine Learning Artificial Intelligence Emerging Technologies 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. |
| title | Smart Profit-Aware Crop Advisory System: Kisan AI |
| topic | Machine Learning Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2605.00133 |