Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform

Yu, Neil; Li, Liujun; Schmitz, Nathan; Tian, Lei; Greenberg, Jonathan A.; Diers, Brian W.

doi:10.1016/j.rse.2016.10.005

Cited by 169 publications

(134 citation statements)

References 45 publications

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Section: Resultssupporting

confidence: 77%

“…Similar to other studies, this experiment shows the ability to use canopy reflectance of specific light bands to characterize relative maturity in soybeans (Christenson et al, 2016;Craft et al, 2019;Yu et al, 2016). Yu et al (2016) processed imagery collected from aerial platforms and predicted soybean maturity from imagery assessment to within 3 d of observed maturity. Christenson et al (2016) collected reflectance data using a spectroradiometer and produced predictive models incorporating multiple wavebands or indices with R 2 values of .43 and .50 and RMSE of 5.51 and 5.19, respectively.…”

Section: Crop Sciencesupporting

confidence: 64%

“…Yu et al. (2016) processed imagery collected from aerial platforms and predicted soybean maturity from imagery assessment to within 3 d of observed maturity. Christenson et al.…”

Section: Resultsmentioning

confidence: 99%

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Modeling canopy senescence to calculate soybean maturity date using NDVI

2020

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Soybeans [Glycine max (L.) Merr.] are the second largest crop in the United States, and cultivar development is important to improve productivity on existing farms. High‐throughput phenotyping can assist breeders in characterizing new lines particularly for phenotypic traits of interest such as crop canopy color, leaf distribution, and seed yield. However, current methods for characterizing the maturity date of soybeans needs to be improved to further leverage high‐throughput phenotyping platforms. The objective of this study was to examine if relative differences in leaf senescence patterns of soybean cultivars could be used to accurately calculate the observed maturity date. A field study was conducted in 2016–2017 at two locations in Ohio. Leaf canopy senescence was measured using the normalized difference vegetation index (NDVI), and observed maturity was measured for each cultivar. Senescence was modeled for each cultivar, and relative differences in senescence calculated from NDVI reductions were compared with the relative differences in observed maturity for each cultivar. The relative differences between cultivars in the date when NDVI was reduced by 85% during senescence (compared with NDVI at the R5 growth stage) were accurate to calculate maturity differences to within 1.5 d of the observed values for 21 of the 24 cultivar × site‐year combinations. This practice has potential to contribute additional data to enhance the high‐throughput phenotyping process using existing equipment (or data being collected) in many programs.

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Section: Resultssupporting

confidence: 77%

Section: Crop Sciencesupporting

confidence: 64%

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Modeling canopy senescence to calculate soybean maturity date using NDVI

2020

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“…Additionally, the bias was calculated (Equation (4). To evaluate the prediction performance for each sampling date, the deviation of the predicted values from the measured height values, scaled by the mean of the measured height values, were used.…”

Section: Methodsmentioning

confidence: 99%

“…Especially UAV systems equipped with RGB (red, green, blue) cameras are widely distributed, but systems with other cameras installed (e.g., multi-spectral cameras) are also getting more and more available. Detailed information about crop health [3], crop biomass development [4], and crop water status [5] have been already successfully extracted from UAV remote sensing for various agriculture crops.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

et al. 2018

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3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R 2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).

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Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

et al. 2023

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High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS‐collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.

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Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform

Cited by 169 publications

References 45 publications

Modeling canopy senescence to calculate soybean maturity date using NDVI

Modeling canopy senescence to calculate soybean maturity date using NDVI

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

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