Lodging markedly reduced the biomass of sweet sorghum via decreasing photosynthesis in saline-alkali field

Guo, Jian Rong; Hai, Fan; Wang, Bao Shan

doi:10.1051/e3sconf/20183802016

Cited by 3 publications

(5 citation statements)

References 22 publications

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“…In addition, the fodder rice varieties for whole crop silage (WCS) such as cultivar 'Tachiaoba', 'Kusahonami', and 'Leaf Star' have also been developed, and the most important factor for the quality of WCS is the high total digestible nutrient yield (TDNY) [27][28][29]. Controlling rice lodging is one of the goals for maintaining yield and sustainable agricultural cultivation because the biomass and grain yield of crops are greatly affected by lodging [30][31][32]. A new feed rice line LTAT-29 (officially registered as a cultivar 'Monster Nokodai 1 , recently) and a new fodder rice line TAT-26 (Figure S2) [33][34][35][36] are characterized by the superior lodging resistance provided by their strong culm, resulting in high yield and biomass production with giant shoots.…”

Section: Introductionmentioning

confidence: 99%

Biofertilizer with Bacillus pumilus TUAT1 Spores Improves Growth, Productivity, and Lodging Resistance in Forage Rice

et al. 2022

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Bacillus pumilus strain TUAT1 is a plant growth-promoting bacterium (PGPB) applied as a biofertilizer, containing its spores, for rice. In this study, we analyzed the short-term effects of biofertilization on plant growth in the nursery and long-term effects on plant vegetative growth, yield, and lodging resistance in paddy fields using animal feed rice (‘Fukuhibiki’ and line LTAT-29 which was recently officially registered as a cultivar ‘Monster Nokodai 1′) and fodder rice (line TAT-26). The effects of the biofertilization were analyzed under two nitrogen treatments and at two transplanting distances in the field. The application of 107 colony forming units (CFU) mL−1 bacterial spore solution to seeds on plant box significantly improved the initial growth of rice. The biofertilizer treatment with this strain at 107 CFU g−1 onto seeds in nursery boxes increased the nitrogen uptake at the early growth of rice in the field, resulting in higher growth at the late vegetative growth stage (e.g., tiller number and plant height). Furthermore, the improvement of growth led to increases of not only yield components such as the total panicle number (TPN) and the number of spikelets in a panicle (NSP) in LTAT-29 but also the straw yield and quality of TAT-26. The lodging resistances of these forage rice plants were also improved due to the increased root development and photosynthesis creating tougher culms.

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Section: Introductionmentioning

confidence: 99%

Biofertilizer with Bacillus pumilus TUAT1 Spores Improves Growth, Productivity, and Lodging Resistance in Forage Rice

et al. 2022

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“…This damage, defined as the displacement of plant stems from an upright position [4], is mainly induced by the strong winds of extreme weather events during the growing season of the crop. Lodging negatively impacts plant morphology [5], physiological processes [6], and growth [7], as well as impeding harvesting activities, to significantly reduce the final above-ground yield of the crop [8][9][10][11]. Lodging damage can also introduce the error or loss of phenotypic data used for selection in breeding, genetic analysis or management decisions [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Most studies on lodging have relied on in situ visual inspection of the crop by domain experts, i.e., agronomists or breeders [7,17]. However, such methods require intensive manual labor and are limited to small spatial areas.…”

Section: Introductionmentioning

confidence: 99%

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

2022

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Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 precision, and 0.83 recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 precision, and 0.76 recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and precision agriculture applications.

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“…This damage, defined as the displacement of plant stems from an upright position [4] is mainly induced by the strong winds of extreme weather events during the growing season of the crop. Lodging negatively impacts plant morphology [5], physiological processes [6], and growth [7], as well as impeding harvesting activities, to significantly reduce final above-ground yield of the crop [8][9][10][11]. Lodging damage can also introduce error or loss of phenotypic data used for selection in breeding, genetic analysis or management decisions [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Most studies on lodging have relied on in-situ visual inspection of the crop by domain experts i.e. agronomists or breeders [7] [15]. But, such methods require intensive manual labor and are limited to small spatial areas.…”

Section: Introductionmentioning

confidence: 99%

Implementing spatio-temporal 3D-convolution neural networks and UAV time series imagery to better predict lodging damage in sorghum.

Varela

Leakey

2022

Preprint

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Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very high-resolution remote sensing image sets collected repeatedly across a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow improved estimation of crop traits, as well as the effects of genetics and the environment upon them. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time-series imagery with 0.88 accuracy, 0.92 precision, and 0.83 recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 precision, and 0.76 recall. Variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. Success of the improved 3D-CNN analysis approach depended on inclusion of before and after data i.e. images collected on dates before and after the lodging event. Integration of geometric and spectral features with 3D-CNN architecture was also key to improved assessment of lodging severity, which is an important and difficult to assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and precision agriculture applications.

show abstract

Lodging markedly reduced the biomass of sweet sorghum via decreasing photosynthesis in saline-alkali field

Cited by 3 publications

References 22 publications

Biofertilizer with Bacillus pumilus TUAT1 Spores Improves Growth, Productivity, and Lodging Resistance in Forage Rice

Biofertilizer with Bacillus pumilus TUAT1 Spores Improves Growth, Productivity, and Lodging Resistance in Forage Rice

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Implementing spatio-temporal 3D-convolution neural networks and UAV time series imagery to better predict lodging damage in sorghum.

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