Abstract
Convenient, efficient, and high-throughput estimation of wheat heading dates is of great significance in plant sciences and agricultural research. However, documenting heading dates is time-consuming, labor-intensive, and subjective on a large-scale field. To overcome these challenges, model- and image-based approaches are used to estimate heading dates. Phenology models usually require complicated parameters calibrations, making it difficult to model other varieties and different locations, while in situ field-image recognition usually requires the deployment of a large amount of observational equipment, which is expensive. Therefore, in this study, we proposed a growth curve-based method for estimating wheat heading dates. The method first generates a height-based continuous growth curve based on five time-series unmanned aerial vehicle (UAV) images captured over the entire wheat growth cycle (>200 d). Then estimate the heading date by generated growth curve. As a result, the proposed method had a mean absolute error of 2.81 d and a root mean square error of 3.49 d for 72 wheat plots composed of different varieties and densities sown on different dates. Thus, the proposed method is straightforward, efficient, and affordable and meets the high-throughput estimation requirements of large-scale fields and underdeveloped areas.
Highlights
The results revealed that Equations (5), (7), and (8) could not fit the growth curves of wheat plant height and could not be used estimate wheat dates.curves
The accuracy estimates of heading date was evaluated using field records
The minimum mean absolute error (MAE) and root mean square error (RMSE) were observed for the equal interval sampling method
Summary
It is estimated that double the present rate of crop production will be needed to meet the needs of the growing population globally by 2050 [2]. The rapid increase in global food production over the past 60 years has been one of the greatest public health achievements in modern history, partly because of technological innovations, including the development of high-yielding crop varieties, production and use of chemical fertilizers and pesticides, as well as agricultural mechanization [3]. Global food production has increased rapidly, a significant component of the population remains undernourished. Identifying and monitoring the phenological stages of crops is essential for breeding new varieties, selecting dominant species, and determining reasonable cultivation methods and accurate field management strategies (irrigation and fertilization) to improve grain yield and quality and promote food security.
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