Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Abstract

Background: The accurate estimation of potato yield at regional scale is crucial for food security, precision agriculture and agricultural sustainable development. Methods: In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results: The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCI red edge ) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R 2 value of 0.8333, and the estimation error about 8%. Conclusion: This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

Combining a Crop Growth Model With CNN for Underground Natural Gas Leakage Detection Using Hyperspectral Imagery

“…Most previous research on hyperspectral vegetation parameter estimation has used traditional machine learning methods, such as support vector machine (SVM) and random forest (RF) [26][27][28]. With the development of deep learning techniques, researchers have found that this data-driven approach is suitable for modeling structurally complex or high-dimensional data [29].…”