"AI - Based Crop cost and Profitability Management: A Human - AI Collaboration Approach to Agricultural financing Planning"

Agriculture serves as the backbone of the Indian economy, yet managing unpredictable cultivation costs and estimating net revenue remain severe challenges for farmers. This project presents an innovative, technology-driven framework integrating Artificial Intelligence with core managerial finance principles- specifically cost accounting, budgeting, and profit forecasting. By processing key agricultural variables (crop variety, seed quality, soil condition, acreage, local climate, and live market pricing), the proposed Al platform systematically estimates input costs (seeds, labor, fertilizer, irrigation, machinery, harvesting) and projects anticipated crop yield and revenue. Emphasizing a Human-in-the-Loop model, the platform pairs algorithmic cost analysis with the farmer's real-world judgment and local domain experience to evaluate alternate crop scenarios (e.g., Paddy vs. Maize) before sowing.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-03
DOI
https://doi.org/10.5281/zenodo.23924346
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

"AI - Based Crop cost and Profitability Management: A Human - AI Collaboration Approach to Agricultural financing Planning"

PRASANTH S
Zenodo (CERN European Organization for Nuclear Research)
Smart Agriculture and AI
article

"AI - Based Crop cost and Profitability Management: A Human - AI Collaboration Approach to Agricultural financing Planning"

PRASANTH S
article en

Abstract

Agriculture serves as the backbone of the Indian economy, yet managing unpredictable cultivation costs and estimating net revenue remain severe challenges for farmers. This project presents an innovative, technology-driven framework integrating Artificial Intelligence with core managerial finance principles- specifically cost accounting, budgeting, and profit forecasting. By processing key agricultural variables (crop variety, seed quality, soil condition, acreage, local climate, and live market pricing), the proposed Al platform systematically estimates input costs (seeds, labor, fertilizer, irrigation, machinery, harvesting) and projects anticipated crop yield and revenue. Emphasizing a Human-in-the-Loop model, the platform pairs algorithmic cost analysis with the farmer's real-world judgment and local domain experience to evaluate alternate crop scenarios (e.g., Paddy vs. Maize) before sowing.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 17%
Smart Agriculture and AI
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