Peanut yield prediction using remote sensing and machine learning approaches based on phenological characteristics

• Peanut yield prediction with GNDVI demonstrated a high accuracy. • Incorporating various phenological parameters enhances peanut yield predictions. • RF models showed improved accuracy when incorporating a complete set of features. Yield prediction of root-fruit crops before harvest is significant for implementing precise field management. However, unlike crops such as wheat and corn, non-destructively predicting the yield of root-fruit crops non-destructively is challenging owing to their edible parts being located underground. Remote sensing offers a potential solution to this problem. Studies on predicting peanut yield through remote sensing are rare. Most of these studies relying on specific vegetation indices, such as the normalized difference vegetation index (NDVI), but have limitations in terms of model accuracy when other phenological parameters influencing peanut yield formation are not considered. 355 peanut yield samples were collected from two distinct cultivation patterns in 2022, 2023 and 2024 and In the study of peanut yield prediction, two modeling methods, linear regression and random forest, were employed to develop prediction models. Considering the contributions of early-stage material accumulation and late- stage material transfer to peanut yield, the results showed that incorporating multiple phenological parameters into peanut yield prediction models enhances accuracy beyond that achieved by models relying solely on early growth stage vegetation indices such as maximum NDVI.. Furthermore, the random forest algorithm has demonstrated its effectiveness in predicting peanut yields, particularly for summer peanuts, as evidenced by its successful application in related studies. The R 2 reached a high of 0.8201, while the lowest MAE and RMSE values were recorded at 0.2878 and 0.4048 t/ha, respectively. This study’s findings have significantly contributed to remote sensing-based yield prediction for root-fruit crops, further refining precision management practices in the cultivating of crops such as peanuts.