Choosing Crops for Profit, Not Just Yield: An AI-Assisted Framework

Many AI tools in agriculture predict crop yield or crop price. They are usually judged on how accurate the prediction is. But a farmer does not only care about accuracy. A farmer cares about profit: the money left after all costs are paid. In this article I propose a simple framework that joins three parts: a yield forecast, a price forecast, and a cost estimate. It ranks crops by expected profit and by risk-adjusted profit. I explain the maths clearly, point out a common mistake (multiplying average yield by average price when the two are related), and show a worked example with a wheat, rice and maize choice for a Punjab-style farm. The numbers in the example are made up to explain the method, and I say so clearly. I also describe how the method should be tested on real data (FAO, NASA POWER, World Bank and local market prices) before anyone claims it works in practice.