Monitoring and zoning soybean maturity using UAV remote sensing

Abstract

Timely and accurate determination of the maturity status during the soybean harvest period is crucial for devising strategic harvesting plans, significantly enhancing the intelligent management of soybean production and minimizing losses. To explore the maturation dynamics of soybeans and pinpoint the precise maturity stage for optimal harvesting, we collected Unmanned Aerial Vehicle (UAV) remote sensing data, along with physical and chemical parameters, during the Beginning maturity(R7)-Full maturity(R8) stages from 2021 to 2022. We analyzed the variations in these parameters throughout the maturation period. Employing UAV multispectral technology and correlation analysis algorithms, we established a relationship between the spectral and physicochemical parameters during the maturation period, which led to the formulation of a soybean maturity evaluation index. Furthermore, we incorporated the concept of precision management zoning. In line with actual field production requirements, this index, combined with real-time moisture content, established a standard for grading soybean maturity. Additionally, our study examined the impact of spatial scale on maturity assessments by conducting a theoretical analysis of regionalized variables and analyzing the spatial correlations and variability of soybean maturity across different field plots. We determined the optimal theoretical model for various sampling distances, thereby establishing feasible zoning ranges. Comparative analysis of zoning algorithms, including Hierarchical Clustering (HC), K-Means, and its optimized versions K-Means++ and Mini Batch K-Means, was conducted using the partition performance index as the criterion. This analysis identified the most suitable algorithm for assessing soybean field maturity, which was subsequently verified through field tests. Results indicated that soybean leaf chlorophyll content and photosynthesis decreased sharply initially, then stabilized at a low rate. Water content in both soybean plants and seeds decreased rapidly before slowing, with plant dehydration rates exceeding those of the seeds. Green Normalized Difference Vegetation Index(GNDVI) showed a positive correlation with various physiological indicators, and soybean maturity was classified into three levels using the unit gridding management method, considering actual moisture content. Spatial correlation analysis of maturity variations in small and large test fields revealed Moran indices ranging from 0.78 to 0.95 and 0.70–0.90, respectively. Optimal detection ranges were determined to be 2.00–30.00 m for small fields and 2.00–67.00 m for large fields. The zoning categories for small and large fields were three and four, respectively. Comparative studies showed that the Mini Batch K-Means algorithm, an optimization of K-Means, achieved Calinski-Harabasz Index(CH) scores and silhouette coefficients of 10521.97, 109508.27, and 0.57, 0.54, respectively, indicating comparable zoning effectiveness to K-Means and K-Means++ and superiority over the HC algorithm. The zoning speeds were 12.50 s and 191.68 s, respectively, underscoring its efficiency in maturity zoning. Furthermore, zoning results corresponded well with actual soybean maturity. Terrain had little impact on soybean maturity, which was more susceptible to slope aspect. This study provides a theoretical foundation and practical guidelines for applying UAV remote sensing technology in determining soybean maturity and optimal harvesting periods, thereby reducing potential harvest losses.