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(searched for: doi:10.1007/s10681-020-02598-7)
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Published: 27 October 2021
by MDPI
Applied Sciences, Volume 11; https://doi.org/10.3390/app112110076

Abstract:
The assessment of rice panicle initiation is crucial for the management of nitrogen fertilizer application that affects yield and quality of grain. The occurrence of panicle initiation could be determined via either green ring, internode-elongation, or a 1–2 mm panicle, and was observed through manual dissection. The quadratic polynomial regression model was used to construct the model of the trend of normalized difference vegetation index-based vegetation indexes (NDVI-based VIs) between pre-tillering and panicle differentiation stages. The slope of the quadratic polynomial regression model tended to be alleviated in the period in which the panicle initiation stage should occur. The results indicated that the trend of the NDVI-based VIs was correlated with panicle initiation. NDVI-based VIs could be a useful indicator to remotely assess panicle initiation.
Jing Zhou, Huawei Mou, , Liakat Ali, Heng Ye, Pengyin Chen,
Plant Phenomics, Volume 2021, pp 1-13; https://doi.org/10.34133/2021/9892570

Abstract:
Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.
Published: 18 February 2021
by MDPI
Abstract:
Okra (Abelmoschus esculentus) is a heat tolerant vegetable crop with high economic and nutritional importance in parts of Asia, Africa, and America. The okra biodiversity held in gene bank collections could be mined for traits for breeding more stress tolerant and nutritional cultivars. An okra core collection of 166 accessions comprising A. esculentus, A. moschatus, A. caillei, and A. manihot has been assembled from the World Vegetable Center germplasm collection (840 accessions) based on diversity analysis with 20 microsatellite markers. A selection of A. esculentus accessions of the core collection (75 accessions) and 20 breeder-selected genotypes have been screened for variation of their response to flooding stress under field conditions using a high throughput phenotyping system. Growth increment per day and changes of physiological indices were measured before, during, and after application of 9 days of flooding stress. Several accessions showed only a small reduction in daily growth increment during flooding. Across the germplasm panel, maintained growth was correlated with maintained normalized differential vegetation index and was negatively correlated with plant senescence index. Accessions with maintained growth and health under flooding were selected for future further analysis and use in breeding.
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