Simulation of Wheat Productivity Using a Model Integrated With Proximal and Remotely Controlled Aerial Sensing Information

Abstract

A crop model incorporating proximal sensing images from a remote-controlled aerial system (RAS) can serve as an enhanced alternative for monitoring field-based geospatial crop productivity. This study aimed to investigate wheat productivity for different cultivars and various nitrogen application regimes and determine the best management practice scenario. We simulated spatiotemporal wheat growth and yield by integrating RAS-based sensing images with a crop-modeling system to achieve the study objective. We conducted field experiments and proximal sensing campaigns to acquire the ground truth data and RAS images of wheat growth conditions and yields. These experiments were performed at Gyeongsang National University (GNU), Jinju, South Gyeongsang province, Republic of Korea (ROK), in 2018 and 2019 and at Chonnam National University (CNU), Gwangju, ROK, in 2018. During the calibration at GNU in 2018, the wheat yields simulated by the modeling system were in agreement with the corresponding measured yields without significant differences (p = 0.27–0.91), according to two-sample t-tests. Furthermore, the yields simulated via this approach were in agreement with the measured yields at CNU in 2018 and at GNU in 2019 without significant differences (p = 0.28–0.86), as evidenced by two-sample t-tests; this proved the validity of the proposed modeling system. This system, when integrated with remotely sensed images, could also accurately reproduce the geospatial variations in wheat yield and growth variables. Given the results of this study, we believe that the proposed crop-modeling approach is applicable for the practical monitoring of wheat growth and productivity at the field level.

Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth

“…Therefore, there have been extensive efforts to advance crop simulation performances by incorporating RS information using various data assimilation approaches involving RS and crop modelling 6 – 8 . For instance, the RS-integrated crop model (RSCM) is based on a hybrid scheme and is used to simulate staple crops, including barley, paddy rice, soybean, and wheat 6 , 9 – 11 . RSCM can incorporate the leaf area index (LAI) or vegetation indices (VIs) from various types of RS data.…”

Yield estimation of summer maize based on multi‐source remote‐sensing data

“…Recently, with the development of sensor technologies, airborne remote sensing and proximal sensing have become important tools for crop condition monitoring, yield prediction, and agricultural management optimization (Darra et al., 2021; Fountas et al., 2020; Tefera et al., 2022). Crop models incorporating proximal sensing images from remote‐controlled aerial systems (RAS) can accurately reproduce geospatial variation in wheat ( Triticum aestivum L.) yield and growth variables (Shin et al., 2021). The spectral indices derived from the small unmanned aerial system (sUAS) were consistent with on‐farm measurements of chlorophyll content and yield in maize ( Zea mays L.), which were most valid for the relationship between spectral signatures and crop health on Malawi farms (Peter et al., 2020).…”