Plant-level prediction of potato yield using machine learning and unmanned aerial vehicle (UAV) multispectral imagery

Abstract This study presents a new method for predicting the underground yield of potato at the plant level, using two key approaches: (1) identifying the critical variables for yield prediction based on plant height and vegetation index (VI) maps derived from unmanned aerial vehicle (UAV) imagery; (2) evaluating the accuracy of predictions for fresh tuber weight (FTW), number of tubers (NMT), and fresh weight per tuber (FWT), using various machine learning (ML) algorithms. During the growing season of 2022, high-resolution red, green, and blue light and multispectral images were collected weekly using a UAV. In total, 648 variables, including first- and second-order statistical parameters, were extracted from the images. Five feature-selection algorithms were used to identify the key variables influencing the predictions of FTW, NMT, and FWT. Furthermore, ML models, including random forest (RF), ridge regression, and support vector machines, were employed to refine the variable sets for ensuring stable yield component predictions. The results highlighted the importance of considering first- and second-order statistical parameters derived from plant height and VI. Second-order statistics were crucial for predicting the FTW and FWT. The RF model demonstrated high prediction accuracy, with R 2 values of 0.57, 0.45, and 0.49 for FTW, NMT, and FWT, respectively, using the best feature selection method. Thus, leveraging RGB and multispectral imagery data recorded that 1.5–2 months before harvest can significantly enhance yield predictions conducted using ML models. The proposed methodology can help farmers growing potatoes or other crops optimize cultivation and predict the yield.