Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Ni, Jun; Yao, Lili; JingChao, Zhang; Cao, Weixing; Zhu, Yan; Tai, Xiuxiang

doi:10.3390/s17030502

Cited by 42 publications

(27 citation statements)

References 29 publications

(27 reference statements)

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“…The results presented here not only confirm that these traits can be used to analyse crop responses to changes in treatment, but also prove that these indicators can be reliably obtained either by MGP or UAV imaging. Analysis of the crop growth as a function of interactions with soil and environmental conditions can subsequently provide customized management plans for farmers to maximize yield [48].…”

Section: Discussionmentioning

confidence: 99%

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

et al. 2018

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Abstract:This study evaluates an aerial and ground imaging platform for assessment of canopy development in a wheat field. The dependence of two canopy traits, height and vigour, on fertilizer treatment was observed in a field trial comprised of ten varieties of spring wheat. A custom-built mobile ground platform (MGP) and an unmanned aerial vehicle (UAV) were deployed at the experimental site for standard red, green and blue (RGB) image collection on five occasions. Meanwhile, reference field measurements of canopy height and vigour were manually recorded during the growing season. Canopy level estimates of height and vigour for each variety and treatment were computed by image analysis. The agreement between estimates from each platform and reference measurements was statistically analysed. Estimates of canopy height derived from MGP imagery were more accurate (RMSE = 3.95 cm, R 2 = 0.94) than estimates derived from UAV imagery (RMSE = 6.64 cm, R 2 = 0.85). In contrast, vigour was better estimated using the UAV imagery (RMSE = 0.057, R 2 = 0.57), compared to MGP imagery (RMSE = 0.063, R 2 = 0.42), albeit with a significant fixed and proportional bias. The ability of the platforms to capture differential development of traits as a function of fertilizer treatment was also investigated. Both imaging methodologies observed a higher median canopy height of treated plots compared with untreated plots throughout the season, and a greater median vigour of treated plots compared with untreated plots exhibited in the early growth stages. While the UAV imaging provides a high-throughput method for canopy-level trait determination, the MGP imaging captures subtle canopy structures, potentially useful for fine-grained analyses of plants.

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Section: Discussionmentioning

confidence: 99%

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

et al. 2018

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“…Mean height [3,9,12,13,15,16,[19][20][21]23,25,28,30,34,[48][49][50]57,58,63,65,[67][68][69][74][75][76] Maximum height [1,3,4,13,28,30,34,48,57,63,65,69] Minimum height [3,28,34,48,57,63,65,69] Median height [12,21,27,48,63,65,…”

Section: Heightmentioning

confidence: 99%

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

Poley

McDermid

2020

Remote Sensing

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Interest in the use of unmanned aerial systems (UAS) to estimate the aboveground biomass (AGB) of vegetation in agricultural and non-agricultural settings is growing rapidly but there is no standardized methodology for planning, collecting and analyzing UAS data for this purpose. We synthesized 46 studies from the peer-reviewed literature to provide the first-ever review on the subject. Our analysis showed that spectral and structural data from UAS imagery can accurately estimate vegetation biomass in a variety of settings, especially when both data types are combined. Vegetation-height metrics are useful for trees, while metrics of variation in structure or volume are better for non-woody vegetation. Multispectral indices using NIR and red-edge wavelengths normally have strong relationships with AGB but RGB-based indices often outperform them in models. Including measures of image texture can improve model accuracy for vegetation with heterogeneous canopies. Vegetation growth structure and phenological stage strongly influence model accuracy and the selection of useful metrics and should be considered carefully. Additional factors related to the study environment, data collection and analytical approach also impact biomass estimation and need to be considered throughout the workflow. Our review shows that UASs provide a capable tool for fine-scale, spatially explicit estimations of vegetation AGB and are an ideal complement to existing ground- and satellite-based approaches. We recommend future studies aimed at emerging UAS technologies and at evaluating the effect of vegetation type and growth stages on AGB estimation.

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“…Previous research has revealed significant differences in the results of UAV RS-based crop growth monitoring due to the use of different sensors [14]. With the exception of a few studies using Lidar [52] and non-imaging active or passive canopy sensors [7,53], most of the UAV-based studies have used imaging spectrometers or multispectral [16,48] and hyperspectral cameras [19]. However, professional imaging sensors, UAV systems, and their supporting software may lead to a high total cost for ordinary consumers and cause challenges for technical promotion.…”

Section: Potential Of Consumer-grade Uav-based Digital Imagery For Crmentioning

confidence: 99%

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

et al. 2019

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Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.

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Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Cited by 42 publications

References 29 publications

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

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