# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 5 Limitations and Future Work  
Traditional wheat yield estimation methods often yield less than ideal precision. In comparison, our study demonstrates that employing different modeling approaches has led to higher accuracy in wheat yield estimation, particularly when utilizing the Random Forest method. This article boasts several key advantages: Firstly, our approach offers ease of data acquisition, with straightforward and user-friendly operations. This makes it very suitable for estimating wheat yield in smaller areas, resulting in reduced labor and resource expenditures. Secondly, our use of Principal Component Analysis to extract key components, combined with a stepwise regression approach, not only enhances efficiency but also improves modeling precision. Thirdly, the incorporation of neural networks and Random Forest algorithms has shown remarkable compatibility with high-spectral data, leading to more accurate yield estimations. Finally, our approach allows for flexibility in selecting the target estimation areas, ensuring high precision. The timely and accurate estimation of wheat yield before harvesting empowers agricultural producers by providing them with the information needed for effective and scientific field management. The method provided in this article, with its enhanced precision and efficiency, underscores the potential for advancing wheat yield estimation and optimizing agricultural practices. The accuracy indicators of the model are discussed, but the outliers and their effects on the results are not analyzed in depth, and future research will focus on the effects of outliers on the modeling results, as well as the effects of specific observations or field conditions on the model. Less accurate models such as multiple linear regression and principal component analysis have been discussed, but the potential value of these models in practical applications has not been explored, and future research will focus on whether specific conditions exist for these models to improve their performance. In the future, the stage of the growth process of wheat at which data is collected, the daily management of crops, the frequency of data collection and the influence of these vegetation index changes over time on yield estimation will be analyzed, a new vegetation index will be explored and modeling algorithms will be adjusted. It is helpful to deeply understand the influence of environmental variables such as climatic conditions and soil characteristics on the modeling accuracy of yield estimation during the whole growth period of wheat. This paper compares the performance of different modeling methods, with random forest and neural network methods performing better, which may be related to the nonlinear nature of the data or the ability of different modeling methods to capture the correlation between vegetation index and wheat yield. Future studies will explore these aspects in depth to improve understanding of the differences in modeling method performance and provide more targeted recommendations for model selection. To improve the usefulness of the study, these models were implemented for the day-to-day management of crops in future studies, including frequency of data collection and integration with other agricultural management practices. This will increase the practical impact of the research and improve its operability. In our study, the use of portable hyperspectral instruments for large-scale wheat yield estimation incurs significant human and material resources. Additionally, the selection of only 30 wheat plots resulted in a relatively small dataset, which may not be entirely representative. Therefore, the results obtained through the application of neural networks and Random Forest methods have certain limitations. Furthermore, there is room for improvement in terms of wheat variety selection, sample size and ensuring the comparability of samples from different regions.