Study on Prediction of Potato Above-Ground Biomass and Yield Based on UAV Visible Light Image

Abstract

Highlights: What are the main findings? Developed a novel, potato-optimized vegetation index (GRCVI) that significantly enhances the accuracy of fractional vegetation cover (FVC) extraction from UAV visible-light imagery under varying field conditions. Proposed an improved single-period SfM method that effectively overcomes soil elevation variability, enabling high-precision plant height estimation without the need for multi-temporal data. What is the implication of the main finding? Establishes a low-cost and efficient UAV-based framework for non-destructively monitoring key potato phenotypic traits (FVC and plant height), above-ground biomass, and ultimately predicting tuber yield. Demonstrates the successful integration of remote sensing features with machine learning (FNN), providing a scalable solution for precision agriculture and high-throughput phenotyping in potato cultivation systems. Potato above-ground biomass (AGB) and tuber yield estimation remain challenging due to the subjectivity of farmer-based assessments, the high data requirements of spectral analysis methods, and the sensitivity of traditional Structure from Motion (SfM) techniques to soil elevation variability. To address these challenges, this study proposes a novel UAV-based visible-light remote sensing framework to estimate the AGB and predict the tuber yield of potato crops. First, a new vegetation index, the Green-Red Combination Vegetation Index (GRCVI), was developed to improve the separability between vegetation and non-vegetation pixels. Second, an improved single-period SfM method was designed to mitigate errors in canopy height estimation caused by terrain variations. Fractional vegetation coverage (FVC) and plant height (PH) derived from UAV imagery were then integrated into a feedforward neural network (FNN) to predict AGB. Finally, potato tuber yield was predicted using polynomial regression based on AGB. Results showed that GRCVI combined with the numerical intersection method and SVM classification achieved FVC extraction accuracy exceeding 95%. The improved SfM method yielded canopy height estimates with R2 values ranging from 0.8470 to 0.8554 and RMSE values below 2.3 cm. The AGB estimation model achieved an R2 of 0.8341 and an RMSE of 19.9 g, while the yield prediction model obtained an R2 of 0.7919 and an RMSE of 47.0 g. This study demonstrates the potential of UAV-based visible-light imagery for cost-effective, non-destructive, and scalable monitoring of potato growth and yield, providing methodological support for precision agriculture and high-throughput phenotyping.