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Tiller fertility is critical for improving grain yield, photosynthesis and nitrogen efficiency in wheat
DING Yong-gang, ZHANG Xin-bo, MA Quan, LI Fu-jian, TAO Rong-rong, ZHU Min, Li Chun-yan, ZHU Xin-kai, GUO Wen-shan, DING Jin-feng
2023, 22 (7): 2054-2066.   DOI: 10.1016/j.jia.2022.10.005
Abstract255)      PDF in ScienceDirect      
Genetic improvement has promoted wheat’s grain yield and nitrogen use efficiency (NUE) during the past decades. Therefore, the current wheat cultivars exhibit higher grain yield and NUE than previous cultivars in the Yangtze River Basin, China since the 2000s. However, the critical traits and mechanisms of the increased grain yield and NUE remain unknown. This study explores the mechanisms underlying these new cultivars’ increased grain yield and NUE by studying 21 local cultivars cultivated for three growing seasons from 2016 to 2019. Significantly positive correlations were observed between grain yield and NUE in the three years. The cultivars were grouped into high (HH), medium (MM), and low (LL) grain yield and NUE groups. The HH group exhibited significantly high grain yield and NUE. High grain yield was attributed to more effective ears by high tiller fertility and greater single-spike yield by increasing postanthesis single-stem biomass. Compared to other groups, the HH group demonstrated a longer leaf stay-green ability and a greater flag leaf photosynthetic rate after anthesis. It also showed higher N accumulation at pre-anthesis, which contributed to increasing N accumulation per stem, including stem and leaf sheath, leaf blade, and unit leaf area at preanthesis, and promoting N uptake efficiency, the main contribution of high NUE. Moreover, tiller fertility was positively related to N accumulation per stem, N accumulation per unit leaf area, leaf stay-green ability, and flag leaf photosynthetic rate, which indicates that improving tiller fertility promoted N uptake, leaf N accumulation, and photosynthetic ability, thereby achieving synchronous improvements in grain yield and NUE. Therefore, tiller fertility is proposed as an important kernel indicator that can be used in the breeding and management of cultivars to improve agricultural efficiency and sustainability.
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Detection and enumeration of wheat grains based on a deep learning method under various scenarios and scales
WU Wei, YANG Tian-le, LI Rui, CHEN Chen, LIU Tao, ZHOU Kai, SUN Cheng-ming, LI Chun-yan, ZHU Xin-kai, GUO Wen-shan
2020, 19 (8): 1998-2008.   DOI: 10.1016/S2095-3119(19)62803-0
Abstract145)      PDF in ScienceDirect      
Grain number is crucial for analysis of yield components and assessment of effects of cultivation measures.  The grain number per spike and thousand-grain weight can be measured by counting grains manually, but it is time-consuming, tedious and error-prone.  Previous image processing algorithms cannot work well with different backgrounds and different sizes.  This study used deep learning methods to resolve the limitations of traditional image processing algorithms.  Wheat grain image datasets were collected in the scenarios of three varieties, six background and two image acquisition devices with different heights, angles and grain numbers, 1 748 images in total.  All images were processed through color space conversion, image flipping and rotation.  The grain was manually annotated, and the datasets were divided into training set, validation set and test set.  We used the TensorFlow framework to construct the Faster Region-based Convolutional Neural Network Model.  Using the transfer learning method, we optimized the wheat grain detection and enumeration model.  The total loss of the model was less than 0.5 and the mean average precision was 0.91.  Compared with previous grain counting algorithms, the grain counting error rate of this model was less than 3% and the running time was less than 2 s.  The model can be effectively applied under a variety of backgrounds, image sizes, grain sizes, shooting angles, and shooting heights, as well as different levels of grain crowding.  It constitutes an effective detection and enumeration tool for wheat grain.  This study provides a reference for further grain testing and enumeration applications.
 
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