Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles

Abstract

The estimation of yield parameters based on early data is helpful for agricultural policymakers and food security. Developments in unmanned aerial vehicle (UAV) platforms and sensor technology help to estimate yields efficiency. Previous studies have been based on less cultivars (<10) and ideal experimental environments, it is not available in practical production. Therefore, the objective of this study was to estimate the yield parameters of soybean (Glycine max (L.) Merr.) under lodging conditions using RGB information. In this study, 17 time point data throughout the soybean growing season in Nanchang, Jiangxi Province, China, were collected, and the vegetation index, texture information, canopy cover, and crop height were obtained by UAV-image processing. After that, partial least squares regression (PLSR), logistic regression (Logistic), random forest regression (RFR), support vector machine regression (SVM), and deep learning neural network (DNN) were used to estimate the yield parameters. The results can be summarized as follows: (1) The most suitable time point to estimate the yield was flowering stage (48 days), which was when most of the soybean cultivars flowered. (2) The multiple data fusion improved the accuracy of estimating the yield parameters, and the texture information has a high potential to contribute to the estimation of yields, and (3) The DNN model showed the best accuracy of training (R2=0.66 rRMSE=32.62%) and validation (R2=0.50, rRMSE=43.71%) datasets. In conclusion, these results provide insights into both best estimate period selection and early yield estimation under lodging condition when using remote sensing.

Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning

“…Photogrammetric techniques are employed to construct digital models of the Earth's surface and perform precise geometric measurements from UAV images, as noted by [10,12,56,57]. After the aerial image processing, DEM is derived by calculating the difference between the DSM and DTM.…”

Enhancing phenotyping efficiency in faba bean breeding: integrating UAV imaging and machine learning

“…However, its performance on the test data set wasn't satisfactory in certain models. This discrepancy could potentially be attributed to the phenomenon of overfitting, where the RF model becomes too specialized to the training set, leading to reduced performance on unseen data (Bai et al, 2022;Xiang-yu et al, 2020). The SVR model demonstrates high accuracy on the test data set due to its robustness, and compatibility with small sample data regression (Elbeltagi et al, 2021;Shafaei & Kisi, 2017;Suykens et al, 2002).…”

“…Li et al, 2021aL. Li et al, , 2021bYang et al, 2022), soybean (Bai et al, 2022) and rice (L. Li et al, 2021aLi et al, , 2021bZhou et al, 2017) that flowering stage can provide more reliable yield prediction by spectral indices. Moreover, the intensive growth period of faba bean occurs during the flowering stage when the crop reaches its highest leaf area index and CO 2 fixation rate (Etemadi et al, 2018).…”

Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing

“…According to different mounting platforms, LiDAR systems used for crop height measurement mainly include terrestrial laser scanning (TLS) [ 14 , 64 ], backpack laser scanning (BLS) [ 78 ], gantry laser scanning (GLS) [ 36 , 61 ], and unmanned-aerial-vehicle laser scanning (ULS) [ 40 , 54 , 77 ]. In contrast to the active LiDAR sensing technologies, passive sensing-based methods (e.g., Multi-view images) can also measure 3D structure through methods like structure from motion (SFM) [ 3 , 18 , 45 , 69 ]. Among the passive sensing-based techniques, digital aerial photogrammetry (DAP) is one of the most popular ways for field CH estimation due to its low cost, high efficiency, and high accuracy comparable to ULS [ 17 , 21 , 75 , 76 ].…”