Non-destructive measurement of canopy cover is an alternative to biomass sampling at anthesis to predict yield of canola-quality Brassica juncea

Pandey, Babu Ram; Burton, W.; Salisbury, P. A.

doi:10.21475/ajcs.2016.10.04.p7103x

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…To justify that LAI extraction is an efficient method of incorporating observations, we provide additional evidence here. By considering the harvest index and dry matter fraction of the crops, the crop yield reported by SPAM can be transformed into the aboveground crop biomass per harvested area (aggregated to the provincial scale), which should be positively correlated with annual maximum crop cover (Pandey et al, ; Paton & Boag, ). As shown in Figure S7, the cropland maximum canopy cover often increases with the application of extracted LAI values, which tended to be more realistic.…”

Section: Discussionmentioning

confidence: 99%

Recent Global Cropland Water Consumption Constrained by Observations

Chen

Feng

et al. 2019

Water Resources Research

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Under current global warming and accelerated population growth scenarios, cropland irrigation water consumption has become a central issue limiting the sustainability of coupled human‐natural systems. This study proposes a new estimate of recent global cropland water consumption constrained by observations and provides attributions for its recent trend. By incorporating observations, including extracted cropland leaf area index and irrigation threshold data, this study provides improved estimates of recent global cropland evapotranspiration and transpiration as well as irrigation water consumption and withdrawal. The global annual consumption and withdrawal of irrigation water are estimated to be approximately 874 and 1,867 km3 (in 2005), respectively. From 2000 to 2014, a rapid increase in cropland irrigation was detected, especially in water‐deficient areas (i.e., hyperarid, arid, and semiarid regions). Climate change, which mainly consists of rising temperature and reduced moisture conditions, is usually distinguished as the major driving factor. Human‐induced increases in crop canopy cover have also contributed to more irrigation in hyperarid and arid regions. This study also provides suggestions for water‐savings‐targeted cropland management in water‐deficient areas based on the transpiration ratio (i.e., ratio of transpiration to evapotranspiration).

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Section: Discussionmentioning

confidence: 99%

Recent Global Cropland Water Consumption Constrained by Observations

Chen

Feng

et al. 2019

Water Resources Research

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“…Researchers used this function to measure perspectives on high throughput phenotyping in developing countries and proved that this method could be applicable in specific situations where research budget is tight. It has been widely tested and proven as an accurate, easy and fast method to estimate canopy cover of durum wheat (Casadesús et al, 2007), barley (Neumann et al, 2015), sorghum (Chung et al, 2017), canola (Pandey et al, 2016), onion (Córcoles et al, 2013), and turf grass (Richardson et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Study of the Relationship between Biomass and Fractional Green Canopy Cover of Two Forage Crops Using Canopeo®

BOUREGA¹,

BOULAARAS²,

LOUAHDI³

et al. 2023

EPHELS

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Rapid and accurate estimation of biomass is crucial for the assessment of the crop nutrition status and the improvement of crop management strategies. The study was conducted in Setif province (Northeast of Algeria). Canopy cover was calculated using Canopeo® application, at stem elongation, booting, and heading stages for triticale, and V5, R1, and R3 for lentil. The results indicate a high correlation between canopy cover and triticale biomass, at the stem elongation stage with R2 = 0.98 and 0.89 under no till and conventional till, respectively. The RMSE was 0.1 and 0.06t/ha, under the two modes. The lowest correlation is seen at heading stage with a value of 0.21 and RMSE of 0.65t/ha, under no till mode. For lentil, the high correlations between are observed at R3 (early pod), and R1 (early bloom) stages, with a coefficients of 0.85 and 0.98, under zero till and conventional respectively. The RMSE was 0.17t/ha. This smartphone application is suitable for assessing triticale and lentil biomass; it is rapid, easy, and could replace the destructive sampling method.

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“…Prediction of crop biomass can be based on a simple linear regression with different VIs [ 32 ], PH [ 33 ], or CC [ 34 ]. However, combining these individual data (predictors) can result in more accurate predictions.…”

Section: Introductionmentioning

confidence: 99%

High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data

Ranđelović,

Đorđević,

Miladinović

et al. 2023

Plant Methods

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Background Biomass accumulation as a growth indicator can be significant in achieving high and stable soybean yields. More robust genotypes have a better potential for exploiting available resources such as water or sunlight. Biomass data implemented as a new trait in soybean breeding programs could be beneficial in the selection of varieties that are more competitive against weeds and have better radiation use efficiency. The standard techniques for biomass determination are invasive, inefficient, and restricted to one-time point per plot. Machine learning models (MLMs) based on the multispectral (MS) images were created so as to overcome these issues and provide a non-destructive, fast, and accurate tool for in-season estimation of soybean fresh biomass (FB). The MS photos were taken during two growing seasons of 10 soybean varieties, using six-sensor digital camera mounted on the unmanned aerial vehicle (UAV). For model calibration, canopy cover (CC), plant height (PH), and 31 vegetation index (VI) were extracted from the images and used as predictors in the random forest (RF) and partial least squares regression (PLSR) algorithm. To create a more efficient model, highly correlated VIs were excluded and only the triangular greenness index (TGI) and green chlorophyll index (GCI) remained. Results More precise results with a lower mean absolute error (MAE) were obtained with RF (MAE = 0.17 kg/m2) compared to the PLSR (MAE = 0.20 kg/m2). High accuracy in the prediction of soybean FB was achieved using only four predictors (CC, PH and two VIs). The selected model was additionally tested in a two-year trial on an independent set of soybean genotypes in drought simulation environments. The results showed that soybean grown under drought conditions accumulated less biomass than the control, which was expected due to the limited resources. Conclusion The research proved that soybean FB could be successfully predicted using UAV photos and MLM. The filtration of highly correlated variables reduced the final number of predictors, improving the efficiency of remote biomass estimation. The additional testing conducted in the independent environment proved that model is capable to distinguish different values of soybean FB as a consequence of drought. Assessed variability in FB indicates the robustness and effectiveness of the proposed model, as a novel tool for the non-destructive estimation of soybean FB.

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Non-destructive measurement of canopy cover is an alternative to biomass sampling at anthesis to predict yield of canola-quality Brassica juncea

Cited by 3 publications

References 31 publications

Recent Global Cropland Water Consumption Constrained by Observations

Recent Global Cropland Water Consumption Constrained by Observations

Study of the Relationship between Biomass and Fractional Green Canopy Cover of Two Forage Crops Using Canopeo®

High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data

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