Cotton Yield Prediction Using Random Forest (2312.02299v1)

Abstract: The cotton industry in the United States is committed to sustainable production practices that minimize water, land, and energy use while improving soil health and cotton output. Climate-smart agricultural technologies are being developed to boost yields while decreasing operating expenses. Crop yield prediction, on the other hand, is difficult because of the complex and nonlinear impacts of cultivar, soil type, management, pest and disease, climate, and weather patterns on crops. To solve this issue, we employ ML to forecast production while considering climate change, soil diversity, cultivar, and inorganic nitrogen levels. From the 1980s to the 1990s, field data were gathered across the southern cotton belt of the United States. To capture the most current effects of climate change over the previous six years, a second data source was produced using the process-based crop model, GOSSYM. We concentrated our efforts on three distinct areas inside each of the three southern states: Texas, Mississippi, and Georgia. To simplify the amount of computations, accumulated heat units (AHU) for each set of experimental data were employed as an analogy to use time-series weather data. The Random Forest Regressor yielded a 97.75% accuracy rate, with a root mean square error of 55.05 kg/ha and an R2 of around 0.98. These findings demonstrate how an ML technique may be developed and applied as a reliable and easy-to-use model to support the cotton climate-smart initiative.

• The paper demonstrates that a Random Forest model achieves 97.75% prediction accuracy on cotton yield, validating ML techniques for precision agriculture.
• It employs accumulated heat units derived from historical and synthetic data to simplify complex climate and soil inputs without losing precision.
• The study underscores ML’s potential in climate-smart agriculture by enabling efficient farm management and informed resource planning.

In the pursuit of sustainable agriculture, researchers are making significant advancements through the incorporation of technology. One area of focus is improving cotton yield predictions, which are crucial for efficient farm management and planning. A paper has harnessed the power of ML to develop a model that predicts cotton yields with impressive accuracy. The primary ML technique used in the paper is the Random Forest (RF) regressor, which has been applied to data gathered from the southern cotton belt of the United States, encompassing decades of research and including recent climate change effects.

The cotton industry is under pressure to find ways to maintain or increase productivity in the face of climate change while reducing environmental impacts. Accurate predictions of crop yields can enable farmers to make informed decisions about resource management, reducing waste and costs. Moreover, these predictions can help in optimizing harvests and guiding policy decisions for food security strategies. The challenge lies in accounting for the complex and nonlinear factors that affect crop growth, such as climate, soil diversity, cultivar selection, and nitrogen levels.

To address these challenges, the paper utilized historical field data and synthetic data derived from a process-based crop model to train the RF model. Rather than using raw time-series weather data, which can be computationally demanding, the paper transformed temperature data into a simpler form called accumulated heat units (AHU). This measure encapsulates temperature over the growth season, which is a vital determinant of cotton growth and ultimately yield. By employing AHU, the researchers simplified the input to the ML model without sacrificing accuracy.

The results of this research are remarkable, with the RF regressor achieving an accuracy rate of 97.75%, a root mean square error (RMSE) of 55.05 kg/ha, and an R^2 value of around 0.98, indicating a very strong correlation between the predicted and actual yields. The paper's findings validate the potential of ML techniques as practical tools in the agriculture industry, particularly in the field of climate-smart initiatives for crops such as cotton.

The paper's success demonstrates not only the potential for integrating ML in agricultural applications but also the effective use of synthetic data to overcome common hurdles in ML model training like limited datasets. Employing both historical and synthesized data, the researchers have shown how ML models can be calibrated to reflect current climate conditions, making their predictions relevant for present-day scenarios. As the paper suggests, this approach can lead to a narrowing gap between cutting-edge technology and the agricultural sector, encouraging further investigation into synthetic data applications in agriculture. The overarching conclusion is that ML, using techniques like the RF regressor, can be a powerful ally in the mission to achieve sustainable and efficient crop production.