Abstract: Accurate prediction of crop prices is crucial for farmers to make informed decisions about agricultural production and trade. It enables them to optimize their planting decisions, determine the harvest time, and plan their sales strategy to maximize profits. Given the increasing volatility of agricultural markets due to climate change and other factors, the significance of crop price prediction has grown in recent years. As a result, sophisticated models that can analyze large amounts of data to provide accurate price forecasts are in high demand. Nowadays, farmers seek to leverage analytics for obtaining the data they need to make actionable insights and informed decisions. Automated farming is becoming more popular among farmers in many countries. Crop price estimation and evaluation are critical to minimize losses and manage the risk of price fluctuations when farming a specific crop type. Falling prices can lead to significant losses for farmers. In this study, we employed the Random Forest Algorithm to analyze past data, predict prices for new data, and estimate crop prices.
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DOI:
10.17148/IJARCCE.2023.125197
