Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery

Chu, Tianguang; Chen, Ruizhi; Landivar, Juan; Maeda, Murilo M.; Yang, Chenghai; Starek, Michael J.

doi:10.1117/1.jrs.10.036018

Cited by 48 publications

(23 citation statements)

References 39 publications

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“…This claim is supported by other studies in the literature that show the potential of UASbased photogrammetric approaches for crop growth monitoring and phenotyping. [33][34][35][36] Furthermore, UAS provides orders of magnitude increase in data collection efficiency at the field-scale relative to manual ground observation approaches. For example, the UAS-SfM approach can provide "plant level" crop height measurements across entire fields at daily intervals (see Fig.…”

Section: Discussionmentioning

confidence: 99%

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Stanton

Starek

Elliott³

et al. 2017

J. Appl. Remote Sens

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"Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment," J. Appl. Remote Sens. 11(2), 026035 (2017), doi: 10.1117/1.JRS.11.026035. Abstract. A small, fixed-wing unmanned aircraft system (UAS) was used to survey a replicated small plot field experiment designed to estimate sorghum damage caused by an invasive aphid. Plant stress varied among 40 plots through manipulation of aphid densities. Equipped with a consumer-grade near-infrared camera, the UAS was flown on a recurring basis over the growing season. The raw imagery was processed using structure-from-motion to generate normalized difference vegetation index (NDVI) maps of the fields and three-dimensional point clouds. NDVI and plant height metrics were averaged on a per plot basis and evaluated for their ability to identify aphid-induced plant stress. Experimental soil signal filtering was performed on both metrics, and a method filtering low near-infrared values before NDVI calculation was found to be the most effective. UAS NDVI was compared with NDVI from sensors onboard a manned aircraft and a tractor. The correlation results showed dependence on the growth stage. Plot averages of NDVI and canopy height values were compared with per-plot yield at 14% moisture and aphid density. The UAS measures of plant height and NDVI were correlated to plot averages of yield and insect density. Negative correlations between aphid density and NDVI were seen near the end of the season in the most damaged crops.

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Section: Discussionmentioning

confidence: 99%

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Stanton

Starek

Elliott³

et al. 2017

J. Appl. Remote Sens

Self Cite

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“…However, data collection frequency has remained a significant issue, as LiDAR sensors and airborne imaging sensors are relatively expensive compared to UAS. Recently, UAS have emerged as an alternate to the satellite, airborne imaging sensors or LiDAR sensors to estimate CC, and this approach is more affordable and could provide higher temporal and spatial resolution [24][25][26][27][28]. UAS-based CC measurements have been efficiently used to estimate LAI [29,30] and have been used as one of the comparison parameters to quantify the difference between various crop management practices throughout the growing season [31].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

et al. 2019

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This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.

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“…To the best of our knowledge, UAV photogrammetry has not yet been applied to estimate cotton and sunflower plant heights in early season, and subsequently, to validate the OBIA algorithm using ground truth data. Although a previous investigation used a similar image-based UAV technology to calculate cotton height [43], no field validation was performed, so this methodology remained non-validated at the early growth stage. In this context, some authors have estimated plant height at a late stage, such as Watanabe et al [44], in barley fields just before harvest, and Varela et al [45], some weeks before maize flowering, obtaining lower or similar R 2 values than our results: 0.52 and 0.63, respectively.…”

Section: Obia-based Crop Height Estimationsmentioning

confidence: 99%

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

et al. 2018

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Accurate and timely detection of weeds between and within crop rows in the early growth stage is considered one of the main challenges in site-specific weed management (SSWM). In this context, a robust and innovative automatic object-based image analysis (OBIA) algorithm was developed on Unmanned Aerial Vehicle (UAV) images to design early post-emergence prescription maps. This novel algorithm makes the major contribution. The OBIA algorithm combined Digital Surface Models (DSMs), orthomosaics and machine learning techniques (Random Forest, RF). OBIA-based plant heights were accurately estimated and used as a feature in the automatic sample selection by the RF classifier; this was the second research contribution. RF randomly selected a class balanced training set, obtained the optimum features values and classified the image, requiring no manual training, making this procedure time-efficient and more accurate, since it removes errors due to a subjective manual task. The ability to discriminate weeds was significantly affected by the imagery spatial resolution and weed density, making the use of higher spatial resolution images more suitable. Finally, prescription maps for in-season post-emergence SSWM were created based on the weed maps-the third research contribution-which could help farmers in decision-making to optimize crop management by rationalization of the herbicide application. The short time involved in the process (image capture and analysis) would allow timely weed control during critical periods, crucial for preventing yield loss.

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Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery

Cited by 48 publications

References 39 publications

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

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