Comparative analysis of machine learning and statistical models for cotton yield prediction in major growing districts of Karnataka, India

Abstract Background Cotton is one of the most important commercial crops after food crops, especially in countries like India, where it’s grown extensively under rainfed conditions. Because of its usage in multiple industries, such as textile, medicine, and automobile industries, it has greater commercial importance. The crop’s performance is greatly influenced by prevailing weather dynamics. As climate changes, assessing how weather changes affect crop performance is essential. Among various techniques that are available, crop models are the most effective and widely used tools for predicting yields. Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka, India, utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors. The artificial neural networks (ANNs) performed superiorly with acceptable yield deviations ranging within ± 10% during both vegetative stage (F1) and mid stage (F2) for cotton. The model evaluation metrics such as root mean square error (RMSE), normalized root mean square error (nRMSE), and modelling efficiency (EF) were also within the acceptance limits in most districts. Furthermore, the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district. Specifically, the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum temperature had a major influence on cotton yield in most of the yield predicted districts. These differences highlighted the differential interactions of weather factors in each district for cotton yield formation, highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka. Conclusions Compared with statistical models, machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield formation at different growth stages. This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield. Thus, the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.